{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting Heads 馃樁\n", "## By Cody Kingham, in collaboration with Christiaan Erwich\n", "\n", "## Problem Description\n", "The ETCBC's BHSA core data does not contain the standard syntax tree format. This also means that syntactic and functional relationships between individual words are not mapped in a transparent or easily accessible way. In some cases, fine-grained relationships are ignored altogether. For example, for a given noun phrase (NP), there is no explicit way of obtaining its head noun (i.e. the noun itself without any modifying elements). This causes numerous problems for research in the realm of semantics. For instance, it is currently very difficult to calculate the complete person, gender, and number (PGN) of a given subject phrase. That is because PGN is stored at the word level only. But this is a very inadequate representation. Phrases in the ETCBC often contain coordinate relationships within the phrase. So even if one selects the first `noun` in the phrase and checks for its PGN value, they may overlook the presence of another noun which makes the phrase plural. Ideally, the phrase itself would have a PGN feature. But before this kind of data is created, it is necessary to separate the head words of a phrase from their modifying elements such as adjectives, determiners, or nouns in construct (genitive) relations.\n", "\n", "A head word can be defined as the word for which a phrase type is named after. Examples of phrase types are `NP` (noun phrase) or `VP` (verb phrase). In this notebook, we experiment with and build the functions stored in `heads.py` in order to export a set of Text-Fabric edge features. The edge features represent a mapping from a phrase node to its head element.\n", "\n", "This goal requires us to think carefully about the way inter-word, semantic relations are reflected in the ETCBC's data. The ETCBC *does* contain some rudimentary semantic embeddings through the so-called [subphrase](https://etcbc.github.io/bhsa/features/hebrew/c/otype). These can be utilized to isolate head words from secondary elements. A subphrase should *not* be thought of as a smaller, embedded phrase, like the ETCBC's phrase-atom (though it sometimes must inadequately fill that role). Rather, the subphrase is a way to encode relationships between words below the level of a phrase(atom), hence \"sub.\" A subphrase can be a single word, or it can be a collection of words. A word can be in multiple subphrases, but can not be in more than 3 (due to the limitations of the data creation program, [parsephrases](http://www.etcbc.nl/datacreation/#ps3.p)).\n", "\n", "## Method\n", "The types of phrases represented in the ETCBC include `NP` (noun phrase), `VP` (verb phrase), `PrNP` (proper noun phrase), `PP` (prepositional phrase), `AdvP` (adverbial phrase), and [eight others](https://etcbc.github.io/bhsa/features/hebrew/c/typ). For some of these types, isolating the head word is a simple affair. By coordinating a word's phrase-dependent part of speech with its enclosing phrase's type, one can identify the head word. For a `VP`, that would mean simply finding the word within the phrase that has a `pdp` (phrase dependent part of speech) value of `verb`. Or for a prepositional phrase, find the word with a `pdp` of `prep`.\n", "\n", "The `NP` and `PrNP`, on the other hand, present special challenges. These phrases often contain multiple words with a modifying relation to the head noun. An example of this is the construct relation (e.g. \"Son of Jacob\"). The problem becomes particularly thorny when relations like the construct are chained together so that one is faced with the choice between multiple potential head nouns.\n", "\n", "To navigate the problem, we must use the feature [`rela` (relationship)](https://etcbc.github.io/bhsa/features/hebrew/c/rela) stored on `subphrase`s in addition to the `pdp` and phrase `type` features. In order to isolate the head word of a `NP`, we look for a word within the phrase that has a `pdp` value of `subs` (i.e. noun). We then obtain a list of all the `subphrase`s which contain that word using the [L.u Text-Fabric method](https://github.com/Dans-labs/text-fabric/wiki/Api#locality). We then use the list of subphrase node numbers to create a list of all subphrase relations containing the word. If the list contains *any* dependent relations, then the word is automatically excluded from being a head word and we can move on to the next candidate. One final check is required for candidate words at the level of the `phrase`: the same procedure described above for `subphrase`s must be performed for `phrase_atom` relations. This means excluding words within a `phrase_atom` with a dependent relation to another `phrase_atom` within the `phrase`. If the head of a *`phrase_atom`* is being calculated, this step is not necessary.\n", "\n", "There are only two possible `subphrase` or `phrase_atom` relations for a valid head word: `NA` or `par`/`Para` (the verb is an exception, which in a handful of cases does have a construct relation). `NA` means that no relation is reflected. The word is independent. The `par` (`subphrase`) and `Para` (`phrase_atom`) stands for parallel relations, i.e. coordinates. While coordinates are not formally the head, they are often an important part of how the grammatical and semantic relations are built. Thus we provide coordinates alongside the head noun. These words require one further test, that is, it must be verified that their mother (using the [edge feature](https://github.com/Dans-labs/text-fabric/wiki/Api#edge-features) \"[mother](https://etcbc.github.io/bhsa/features/hebrew/c/mother.html)\") is itself a head word. To do this step thus requires us to keep track of those words within the phrase which have been validated. We can do so with a simple list.\n", "\n", "## Results\n", "The function `get_heads` produces head word nodes on supplied phrase(atom) nodes. The results have been manually inspected for consistency.\n", "\n", "For phrase types other than the noun phrase, the results are very accurate. Some phrase types, like the conjunction phrase, do have unexpected forms. For instance, the phrase 讘注讘讜专 is coded as a conjunction phrase in the BHSA; in it, there is actually no word with a part of speech of `conjunction`. These kinds of cases are easily accounted for by making exceptions in the set of acceptable parts of speech.\n", "\n", "For noun phrases, the situation is different. In the majority of cases, the results are good. But there are a handful of cases that simply cannot be addressed using the current ETCBC data model without a solution that exceeds the bounds of this current project. The reason is that the current model does not transparently encode hierarchy between phrases and embedded phrases. For instance, both phrase atoms and subphrases have *some* overlapping features. But what is the relationship of a phrase atom to a subphrase? Or, what is the relation of one subphrase to another? These are only coded implicitly in the data. In reality, there are `subphrases` embedded within the ETCBC's subphrases which are not even registered in the BHSA data. While phrase atoms receive type codes, subphrases do not. Yet, subphrases are `phrases` too, which should also have type codes. Another problem is that the precise level of embedding for the subphrases are not provided. Subphrases are presented as equal constituents, even though some subphrases are contained within others. These kinds of problems make a simple method, such as applied here, inadequate. But more importantly, they highlight the shortcomings of the ETCBC data model.\n", "\n", "The members of the ETCBC are aware of the inadequacy of that data model to represent complex phrases, and a change is in the pipeline to address it. However, it remains to be seen how long those changes might take. For now, the functions produced and modified in this NB will suffice to provide a temporary solution for those who require head words from BHSA phrases." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code Development\n", "\n", "Below we experiment with the code and develop the functions that will extract the head nouns. This involves a good deal of manual inspections of the results before exporting the Text-Fabric features.\n", "\n", "The code is written immediately below. Associated questions that arise while writing or evaluating the code are contained in the subsequent section." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**Documentation:** BHSA Character table Feature docs BHSA API Text-Fabric API 6.4.6 Search Reference" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Loaded features:\n", "book@ll book chapter function gloss label language lex ls number otype pdp rela sp typ verse voc_lex voc_lex_utf8 vs vt mother oslots
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "This notebook online:\n", "NBViewer\n", "GitHub\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import collections, os, sys, random\n", "from pprint import pprint\n", "from tf.fabric import Fabric\n", "from tf.extra.bhsa import Bhsa\n", "\n", "# load Text-Fabric and data\n", "data_loc = [\"~/github/etcbc/bhsa/tf/c\"]\n", "TF = Fabric(locations=data_loc, silent=True)\n", "api = TF.load(\n", " \"\"\"\n", " book chapter verse\n", " typ pdp rela mother \n", " function lex sp ls\n", " \"\"\",\n", " silent=True,\n", ")\n", "\n", "F, E, T, L = api.F, api.E, api.T, api.L # TF data methods\n", "B = Bhsa(api, name=\"getting_heads\", version=\"c\") # BHSA visualizer" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "lines_to_end_of_cell_marker": 2 }, "outputs": [], "source": [ "def get_heads(phrase):\n", " \"\"\"\n", " Extracts and returns the heads of a supplied\n", " phrase or phrase atom based on that phrase's type\n", " and the relations reflected within the phrase.\n", "\n", " --input--\n", " phrase(atom) node number\n", "\n", " --output--\n", " tuple of head word node(s)\n", " \"\"\"\n", "\n", " # mapping from phrase type to good part of speech values for heads\n", " head_pdps = {\n", " \"VP\": {\"verb\"}, # verb\n", " \"NP\": {\"subs\", \"adjv\", \"nmpr\"}, # noun\n", " \"PrNP\": {\"nmpr\", \"subs\"}, # proper-noun\n", " \"AdvP\": {\"advb\", \"nmpr\", \"subs\"}, # adverbial\n", " \"PP\": {\"prep\"}, # prepositional\n", " \"CP\": {\"conj\", \"prep\"}, # conjunctive\n", " \"PPrP\": {\"prps\"}, # personal pronoun\n", " \"DPrP\": {\"prde\"}, # demonstrative pronoun\n", " \"IPrP\": {\"prin\"}, # interrogative pronoun\n", " \"InjP\": {\"intj\"}, # interjectional\n", " \"NegP\": {\"nega\"}, # negative\n", " \"InrP\": {\"inrg\"}, # interrogative\n", " \"AdjP\": {\"adjv\"}, # adjective\n", " }\n", "\n", " # get phrase-head's part of speech value and list of candidate matches\n", " phrase_type = F.typ.v(phrase)\n", " head_candidates = [\n", " w for w in L.d(phrase, \"word\") if F.pdp.v(w) in head_pdps[phrase_type]\n", " ]\n", "\n", " # VP with verbs require no further processing, return the head verb\n", " if phrase_type == \"VP\":\n", " return tuple(head_candidates)\n", "\n", " # go head-hunting!\n", " heads = []\n", "\n", " for word in head_candidates:\n", "\n", " # gather the word's subphrase (+ phrase_atom if otype is phrase) relations\n", " word_phrases = list(L.u(word, \"subphrase\"))\n", " word_phrases += (\n", " list(L.u(word, \"phrase_atom\"))\n", " if (F.otype.v(phrase) == \"phrase\")\n", " else list()\n", " )\n", " word_relas = set(F.rela.v(phr) for phr in word_phrases) or {\"NA\"}\n", "\n", " # check (sub)phrase relations for independency\n", " if word_relas - {\"NA\", \"par\", \"Para\"}:\n", " continue\n", "\n", " # check parallel relations for independency\n", " elif word_relas & {\"par\", \"Para\"} and mother_is_head(word_phrases, heads):\n", " this_head = find_quantified(word) or find_attributed(word) or word\n", " heads.append(this_head)\n", "\n", " # save all others as heads, check for quantifiers first\n", " elif word_relas == {\"NA\"}:\n", " this_head = find_quantified(word) or find_attributed(word) or word\n", " heads.append(this_head)\n", "\n", " return tuple(sorted(set(heads)))\n", "\n", "\n", "def mother_is_head(word_phrases, previous_heads):\n", "\n", " \"\"\"\n", " Test and validate parallel relationships for independency.\n", " Must gather the mother for each relation and check whether\n", " the mother contains a head word.\n", "\n", " --input--\n", " * list of phrase nodes for a given word (includes subphrases)\n", " * list of previously approved heads\n", "\n", " --output--\n", " boolean\n", " \"\"\"\n", "\n", " # get word's enclosing phrases that are parallel\n", " parallel_phrases = [ph for ph in word_phrases if F.rela.v(ph) in {\"par\", \"Para\"}]\n", " # get the mother for the parallel phrases\n", " parallel_mothers = [E.mother.f(ph)[0] for ph in parallel_phrases]\n", " # get mothers' words, by mother\n", " parallel_mom_words = [set(L.d(mom, \"word\")) for mom in parallel_mothers]\n", " # test for head in each mother\n", " test_mothers = [\n", " bool(phrs_words & set(previous_heads)) for phrs_words in parallel_mom_words\n", " ]\n", "\n", " return all(test_mothers)\n", "\n", "\n", "def find_quantified(word):\n", "\n", " \"\"\"\n", " Check whether a head candidate is a quantifier (e.g. 讻诇).\n", " If it is, find the quantified noun if there is one.\n", " Quantifiers are connected with the modified noun\n", " either by a subphrase relation of \"rec\" for nomen\n", " regens. In this case, the quantifier word node is the\n", " mother itself. In other cases, the noun is related to the\n", " number via the `atr` (attributive) subphrase relation. In this\n", " case, the edge relation is connected from the substantive\n", " to the number's subphrase.\n", "\n", " --input--\n", " word node\n", "\n", " --output--\n", " new word node or None\n", " \"\"\"\n", "\n", " custom_quants = {\n", " \"KL/\",\n", " \"M 5:\n", "\n", " heads = get_heads(phrase)\n", "\n", " examples.append(heads)\n", "\n", "len(examples)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "random.shuffle(examples) # get samples at random" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "\n", "\n", "**phrase** *1*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n", "\n", "
\n", " phrase Subj NP\n", "
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\n", "\n", "
\n", "
讝指譅讘
\n", "
subs flow qal ptca
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\n", "
讜旨
\n", "
conj and
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诪职爪止专指譃注
\n", "
subs have skin-disease pual ptcp
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\n", "\n", "
\n", " phrase Subj NP\n", "
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\n", "\n", "
\n", "
讜址
\n", "
conj and
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\n", "\n", "
\n", "\n", "
\n", " phrase Subj NP\n", "
\n", "
\n", "\n", "
\n", "
讞植住址专志
\n", "
adjv lacking
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\n", "\n", "
\n", "
诇指纸讞侄诐變
\n", "
subs bread
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\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *7*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Subj NP\n", "
\n", "
\n", "\n", "
\n", "
讙址诐志
\n", "
advb even
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\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
诪侄旨吱诇侄讱职謾
\n", "
subs king
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
讻指诇志
\n", "
subs whole
\n", "\n", "\n", "
\n", "\n", "
\n", "
注植讘指讚指謹讬讜
\n", "
subs servant
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *8*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Objc NP\n", "
\n", "
\n", "\n", "
\n", "
住讜旨住执芝讬诐
\n", "
subs horse
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\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
专侄謻讻侄讘
\n", "
subs chariot
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\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
讞址郑讬执诇
\n", "
subs power
\n", "\n", "\n", "
\n", "\n", "
\n", "
讻指旨讘值謶讚
\n", "
adjv heavy
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\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *9*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Objc NP\n", "
\n", "
\n", "\n", "
\n", "
拽止芝讜诇
\n", "
subs sound
\n", "\n", "\n", "
\n", "\n", "
\n", "
专侄吱讻侄讘謾
\n", "
subs chariot
\n", "\n", "\n", "
\n", "\n", "
\n", "
拽止郑讜诇
\n", "
subs sound
\n", "\n", "\n", "
\n", "\n", "
\n", "
住謹讜旨住
\n", "
subs horse
\n", "\n", "\n", "
\n", "\n", "
\n", "
拽止謻讜诇
\n", "
subs sound
\n", "\n", "\n", "
\n", "\n", "
\n", "
讞址郑讬执诇
\n", "
subs power
\n", "\n", "\n", "
\n", "\n", "
\n", "
讙指旨讚止謶讜诇
\n", "
adjv great
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\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *10*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Subj NP\n", "
\n", "
\n", "\n", "
\n", "
专址讘值旨吱讬
\n", "
subs chief
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
诪侄旨譁诇侄讱职
\n", "
subs king
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜址
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
注植砖指讉专指吱讛
\n", "
subs ten
\n", "\n", "\n", "
\n", "\n", "
\n", "
讗植谞指砖执讈芝讬诐
\n", "
subs man
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *11*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Subj NP\n", "
\n", "
\n", "\n", "
\n", "
讚职旨讙值郑讬
\n", "
subs fish
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
讬指旨诐蜘
\n", "
subs sea
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
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\n", "\n", "
\n", "
注止吱讜祝
\n", "
subs birds
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
砖指旨讈诪址譁讬执诐
\n", "
subs heavens
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
讞址讬址旨郑转
\n", "
subs wild animal
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
砖指旨讉讚侄謼讛
\n", "
subs open field
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
讻指诇志
\n", "
subs whole
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛指
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
专侄吱诪侄砖讉謾
\n", "
subs creeping animals
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase Subj NP\n", "
\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase Subj NP\n", "
\n", "
\n", "\n", "
\n", "
讻止诇謾
\n", "
subs whole
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛指纸
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
讗指讚指謹诐
\n", "
subs human, mankind
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *12*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Subj NP\n", "
\n", "
\n", "\n", "
\n", "
讻职旨讘止讜讚蜘
\n", "
subs weight
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
讘址旨吱讬执转
\n", "
subs house
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
讝侄旨证讛
\n", "
prde this
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛指
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
讗址纸讞植专止讜谉謾
\n", "
adjv at the back
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *13*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Subj NP\n", "
\n", "
\n", "\n", "
\n", "
注指专侄謻讬讛指
\n", "
subs town
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase Subj NP\n", "
\n", "
\n", "\n", "
\n", "
住职讘执讬讘止转侄謶讬讛指
\n", "
subs surrounding
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase Subj NP\n", "
\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
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\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase Subj NP\n", "
\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
谞侄旨芝讙侄讘
\n", "
subs south
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
砖职旨讈驻值诇指謻讛
\n", "
subs low land
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *14*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Time NP\n", "
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\n", "\n", "
\n", "
讬指诪执郑讬诐
\n", "
subs day
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\n", "\n", "
\n", "
专址讘执旨謹讬诐
\n", "
adjv much
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\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase Time NP\n", "
\n", "
\n", "\n", "
\n", "
砖职讈诪止讜谞执芝讬诐
\n", "
subs eight
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\n", "\n", "
\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
诪职讗址謻转
\n", "
subs hundred
\n", "\n", "\n", "
\n", "\n", "
\n", "
讬止纸讜诐變
\n", "
subs day
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\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *15*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Objc NP\n", "
\n", "
\n", "\n", "
\n", "
拽指郑诇
\n", "
subs voice
\n", "\n", "\n", "
\n", "\n", "
\n", "
拽址专职谞指郑讗
\n", "
subs horn
\n", "\n", "\n", "
\n", "\n", "
\n", "
诪址譅砖职讈专止拽执讬转指讗
\n", "
subs pipe
\n", "\n", "\n", "
\n", "\n", "
\n", "
拽址转职专止吱讜住
\n", "
subs zither
\n", "\n", "\n", "
\n", "\n", "
\n", "
砖址讉讘职旨讻指证讗
\n", "
subs sambuca
\n", "\n", "\n", "
\n", "\n", "
\n", "
驻职住址谞职转值旨专执讬谉謾
\n", "
subs psaltery
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
住讜旨驻止旨郑谞职讬指謹讛
\n", "
subs symphony
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
讻止謻诇
\n", "
subs whole
\n", "\n", "\n", "
\n", "\n", "
\n", "
讝职谞值郑讬
\n", "
subs sort
\n", "\n", "\n", "
\n", "\n", "
\n", "
讝职诪指专指謶讗
\n", "
subs music for strings
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *16*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Objc NP\n", "
\n", "
\n", "\n", "
\n", "
讙址郑诐
\n", "
advb even
\n", "\n", "\n", "
\n", "\n", "
\n", "
讗直纸诇止讛值讬讛侄帧诐
\n", "
subs god(s)
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase Objc NP\n", "
\n", "
\n", "\n", "
\n", "
注执诐志
\n", "
prep with
\n", "\n", "\n", "
\n", "\n", "
\n", "
谞职住执纸讻值讬讛侄诐蜘
\n", "
subs libation
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase Objc NP\n", "
\n", "
\n", "\n", "
\n", "
注执诐志
\n", "
prep with
\n", "\n", "\n", "
\n", "\n", "
\n", "
讻职旨诇值吱讬
\n", "
subs tool
\n", "\n", "\n", "
\n", "\n", "
\n", "
讞侄诪职讚指旨转指譁诐
\n", "
subs what is desirable
\n", "\n", "\n", "
\n", "\n", "
\n", "
讻侄旨支住侄祝
\n", "
subs silver
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
讝指讛指譀讘
\n", "
subs gold
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *17*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Objc NP\n", "
\n", "
\n", "\n", "
\n", "
诪执砖职讈拽址譀诇
\n", "
subs weight
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
讻侄旨芝住侄祝
\n", "
subs silver
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
讝指旨讛指謻讘
\n", "
subs gold
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
讻值旨诇执謶讬诐
\n", "
subs tool
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\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *18*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n", "\n", "
\n", " phrase Cmpl PP\n", "
\n", "
\n", "\n", "
\n", "
注址謿诇
\n", "
prep upon
\n", "\n", "\n", "
\n", "\n", "
\n", "
讙指旨讗殖诇值郑讬
\n", "
subs defilement
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\n", "\n", "
\n", "
讛址
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art the
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\n", "\n", "
\n", "
讻职旨讛只谞指旨謹讛
\n", "
subs priesthood
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\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase Cmpl PP\n", "
\n", "
\n", "\n", "
\n", "
讜旨
\n", "
conj and
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\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase Cmpl PP\n", "
\n", "
\n", "\n", "
\n", "
讘职专执芝讬转
\n", "
subs covenant
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
讻职旨讛只谞指旨謻讛
\n", "
subs priesthood
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "
诇职讜执讬执旨纸诐變
\n", "
subs Levite
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\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *19*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase Adju NP\n", "
\n", "
\n", "\n", "
\n", "
讗址专职讘指旨注执吱讬诐
\n", "
subs four
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\n", "\n", "
\n", "
讜职
\n", "
conj and
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\n", "\n", "
\n", "
讗址专职讘指旨注指芝讛
\n", "
subs four
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\n", "\n", "
\n", "
讗侄譀诇侄祝
\n", "
subs thousand
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\n", "\n", "
\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
砖职讈讘址注志
\n", "
subs seven
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\n", "\n", "
\n", "
诪值讗止芝讜转
\n", "
subs hundred
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\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
砖执讈砖执旨讈謻讬诐
\n", "
subs six
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\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *20*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n", "\n", "
\n", " phrase Objc NP\n", "
\n", "
\n", "\n", "
\n", "
讻执旨讻址旨专志
\n", "
subs disk
\n", "\n", "\n", "
\n", "\n", "
\n", "
诇侄謹讞侄诐
\n", "
subs bread
\n", "\n", "\n", "
\n", "\n", "
\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "
讗侄砖职讈驻指旨謻专
\n", "
subs <type of cake>
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\n", "\n", "
\n", "
讜址
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conj and
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\n", "\n", "
\n", "
讗植砖执讈讬砖指纸讈讛變
\n", "
subs raisin cake
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "B.show(examples[:20], condenseType=\"phrase\") # uncomment me" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Discovery\n", "\n", "The queries which follow were written at different times during the code construction for the heads algorithm.\n", "\n", "In this section, important questions were asked whose answers are needed to ensure the code is written correctly. The BHSA data is queried to answer them. These are questions like, \"Do we need to check for relational independency for only noun phrases?\" (no); and \"does every phrase type have a word with a corresponding `pdp`?\" (no).\n", "\n", "### Make definitions available for exploration:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "# mapping from phrase type to its head part of speech\n", "type_to_pdp = {\n", " \"VP\": \"verb\", # verb\n", " \"NP\": \"subs\", # noun\n", " \"PrNP\": \"nmpr\", # proper-noun\n", " \"AdvP\": \"advb\", # adverbial\n", " \"PP\": \"prep\", # prepositional\n", " \"CP\": \"conj\", # conjunctive\n", " \"PPrP\": \"prps\", # personal pronoun\n", " \"DPrP\": \"prde\", # demonstrative pronoun\n", " \"IPrP\": \"prin\", # interrogative pronoun\n", " \"InjP\": \"intj\", # interjectional\n", " \"NegP\": \"nega\", # negative\n", " \"InrP\": \"inrg\", # interrogative\n", " \"AdjP\": \"adjv\",\n", "} # adjective" ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 2 }, "source": [ "### Test for non-NP phrases with valid `pdp` but invalid head\n", "\n", "These tests demonstrate that subphrase relation checks are also needed for phrase types besides noun phrases. The only valid subphrase/phrase_atom relations for any potential head word is either `NA` or `par`/`Para`. While a few phrase types do not need additional relational checks, e.g. personal pronoun phrases, we can go ahead and consistently handle all phrases in the same way.\n", "\n", "The only exception to the above rule is the `VP`, for which there are 14 cases of the `VP`'s head word (verb) that is also in a subphrase with a regens (`rec`) relation.\n", "\n", "The operational question of these tests was:\n", "> Are there cases in which a non-NP phrase(atom) contains a word with the corresponding `pdp` value, but which is probably not a head?\n", "\n", "To answer the question, we first survey all cases where the phrase type's head candidate is in a subphrase with a relation that is not normally \"independent.\" Based on the survey, we manually check the most pertinent phrase types and results. The tests reveal that, indeed, relation checks are needed for many phrase types." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def test_pdp_safe(phrase_object=\"phrase_atom\"):\n", "\n", " \"\"\"\n", " Make a survey of phrase types and their matching `pdp` words,\n", " count what kinds of subphrase relations these words\n", " occurr in. The survey can then be used to investigate\n", " whether phrase types besides noun phrases require relationship\n", " checks for independency.\n", " \"\"\"\n", "\n", " pdp_relas_survey = collections.defaultdict(lambda: collections.Counter())\n", " headless = 0\n", "\n", " for phrase in F.otype.s(phrase_object):\n", "\n", " typ = F.typ.v(phrase) # phrase type\n", "\n", " head_pdp = type_to_pdp[typ]\n", "\n", " maybe_heads = [w for w in L.d(phrase, \"word\") if F.pdp.v(w) == head_pdp]\n", "\n", " # this check shows that many\n", " # phrases don't have a word\n", " # with a corresponding pdp!\n", " if not maybe_heads:\n", " headless += 1\n", "\n", " # survey the candidate heads' relations\n", " for word in maybe_heads:\n", "\n", " head_name = typ + \"|\" + head_pdp\n", " subphrases = L.u(word, \"subphrase\")\n", " sp_relas = (\n", " set(F.rela.v(sp) for sp in subphrases) if subphrases else {\"NA\"}\n", " ) # <- handle cases without any subphrases (i.e. verbs)\n", "\n", " pdp_relas_survey[head_name].update(sp_relas)\n", "\n", " print(f\"{phrase_object}s without matching pdp: {headless}\\n\")\n", " print(\"subphrase relation survey: \")\n", " for name, rela_counts in pdp_relas_survey.items():\n", "\n", " print(name)\n", "\n", " for r, count in rela_counts.items():\n", " print(\"\\t\", r, \"-\", count)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "phrase_atoms without matching pdp: 847\n", "\n", "subphrase relation survey: \n", "PP|prep\n", "\t NA - 64521\n", "\t par - 3824\n", "\t adj - 42\n", "\t rec - 8\n", "VP|verb\n", "\t NA - 69010\n", "\t rec - 14\n", "\t par - 1\n", "NP|subs\n", "\t NA - 53881\n", "\t par - 5868\n", "\t rec - 11628\n", "\t adj - 2936\n", "\t atr - 69\n", "CP|conj\n", "\t NA - 53859\n", "AdvP|advb\n", "\t NA - 5131\n", "\t par - 102\n", "\t mod - 49\n", "\t adj - 1\n", "AdjP|adjv\n", "\t NA - 1848\n", "\t par - 135\n", "\t atr - 5\n", "\t adj - 3\n", "\t rec - 1\n", "InjP|intj\n", "\t NA - 1872\n", "\t par - 11\n", "DPrP|prde\n", "\t NA - 790\n", "PrNP|nmpr\n", "\t NA - 11794\n", "\t par - 1478\n", "\t adj - 210\n", "\t rec - 83\n", "NegP|nega\n", "\t NA - 6742\n", "PPrP|prps\n", "\t NA - 4468\n", "\t par - 9\n", "IPrP|prin\n", "\t NA - 797\n", "\t par - 1\n", "InrP|inrg\n", "\t NA - 1288\n", "\t par - 3\n" ] } ], "source": [ "# for phrase_atoms\n", "test_pdp_safe()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "phrases without matching pdp: 679\n", "\n", "subphrase relation survey: \n", "PP|prep\n", "\t NA - 62315\n", "\t par - 3678\n", "\t adj - 42\n", "\t rec - 9\n", "VP|verb\n", "\t NA - 69010\n", "\t rec - 14\n", "\t par - 1\n", "NP|subs\n", "\t NA - 50092\n", "\t par - 5808\n", "\t rec - 11214\n", "\t adj - 2927\n", "\t atr - 51\n", "CP|conj\n", "\t NA - 52544\n", "AdvP|advb\n", "\t NA - 5083\n", "\t par - 101\n", "\t mod - 46\n", "\t adj - 1\n", "AdjP|adjv\n", "\t NA - 1800\n", "\t par - 118\n", "\t atr - 5\n", "\t adj - 3\n", "\t rec - 1\n", "InjP|intj\n", "\t NA - 1872\n", "\t par - 11\n", "DPrP|prde\n", "\t NA - 791\n", "PrNP|nmpr\n", "\t NA - 11138\n", "\t par - 1380\n", "\t rec - 1267\n", "\t adj - 209\n", "NegP|nega\n", "\t NA - 6742\n", "PPrP|prps\n", "\t NA - 4388\n", "\t par - 9\n", "IPrP|prin\n", "\t NA - 797\n", "\t par - 1\n", "InrP|inrg\n", "\t NA - 1288\n", "\t par - 3\n" ] } ], "source": [ "# and for phrases\n", "test_pdp_safe(phrase_object=\"phrase\")" ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 2 }, "source": [ "^ These surveys tell us that for several of these phrase types, e.g. `InjP`, we can automatically take the word with the `pdp` value that corresponds with its phrase type as the head.\n", "\n", "There are also quite a few cases where the phrase type does not have a word with a matching `pdp` value: 837 for phrase atoms and 670 for phrases. In the subsequent section we will run tests to find out why this is the case.\n", "\n", "Back to the question of this section: There are 14 examples of `VP` with verbs that have a `rec` (nomen regens) relation. Are these heads or not? We check now..." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "def find_and_show(search_pattern):\n", " results = sorted(B.search(search_pattern))\n", " print(len(results), \"results\")\n", " B.show(results, end=20, condenseType=\"phrase\", withNodes=True)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.86s 14 results\n", "14 results\n" ] }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *1*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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讘职旨
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注值郑转
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诇执讚职转指旨謶讛旨
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verb bear qal infc
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prep in
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讛址专职讗止转职讱指郑
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verb see hif infc
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诪执
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prep from
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砖职旨讈谞址转謾
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\n", "68051\n", "
讛执诪指旨郑讻职专止讜
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verb sell nif infc
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诪执
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prep from
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讚值旨讬志
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讘止芝讗
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verb come qal infc
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诪执
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prep from
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讚值旨郑讬
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subs sufficiency
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注指讘职专止謹讜
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verb pass qal infc
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讘执旨
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prep in
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\n", "206122\n", "
转职讞执诇址旨转謾
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subs beginning
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砖执讈讘职转指旨郑诐
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verb sit qal infc
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讘执旨
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prep in
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砖职讈谞址郑转
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subs year
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诪指诇职讻止謼讜
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verb be king qal infc
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诪执
\n", "
prep from
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讚值旨证讬
\n", "
subs sufficiency
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\n", "221148\n", "
注指讘职专止讜謾
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verb pass qal infc
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诪执
\n", "
prep from
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讚值旨证讬
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subs sufficiency
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\n", "251011\n", "
讚址讘职旨专执讬謾
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verb speak piel infc
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\n", " phrase 804887 PreS VP\n", "
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诪执
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prep from
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拽止旨郑讜诇
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subs sound
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\n", "261525\n", "
谞执驻职诇指謹诐
\n", "
verb fall qal infc
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\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *11*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "810542\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 810542 PreC VP\n", "
\n", "
\n", "\n", "
\n", "270676\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "270677\n", "
讘职旨诇执郑讬
\n", "
subs destruction
\n", "\n", "\n", "
\n", "\n", "
\n", "270678\n", "
注止讜讘值謹专
\n", "
verb pass qal ptca
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *12*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "834935\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 834935 Pred VP\n", "
\n", "
\n", "\n", "
\n", "310033\n", "
诪值
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "310034\n", "
讗值郑讬谉
\n", "
subs <NEG>
\n", "\n", "\n", "
\n", "\n", "
\n", "310035\n", "
注止謼讜讚
\n", "
subs duration
\n", "\n", "\n", "
\n", "\n", "
\n", "310036\n", "
驻职旨谞止讜转謾
\n", "
verb turn qal infc
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *13*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "902929\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 902929 PreS VP\n", "
\n", "
\n", "\n", "
\n", "422921\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "422922\n", "
诇职旨
\n", "
prep to
\n", "\n", "\n", "
\n", "\n", "
\n", "422923\n", "
讘址譃讚
\n", "
subs linen, part, stave
\n", "\n", "\n", "
\n", "\n", "
\n", "422924\n", "
讛执转职讬址讞职砖指讉郑诐
\n", "
verb register hit infc
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *14*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "903656\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 903656 PreS VP\n", "
\n", "
\n", "\n", "
\n", "424337\n", "
诇执
\n", "
prep to
\n", "\n", "\n", "
\n", "\n", "
\n", "424338\n", "
驻职谞值謻讬
\n", "
subs face
\n", "\n", "\n", "
\n", "\n", "
\n", "424339\n", "
讛执讻指旨谞职注止謶讜
\n", "
verb be humble nif infc
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# run notebook locally to see HTML-formatted results for the below searches\n", "\n", "\n", "rec_verbs = \"\"\"\n", "\n", "phrase_atom typ=VP\n", " subphrase rela=rec\n", " word pdp=verb\n", "\"\"\"\n", "\n", "find_and_show(rec_verbs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In all 14 results, the verb serves as the true head word of the `VP`.\n", "\n", "*Note: The verb will prove to be an exception, as all other words in a `rec` relation are not head words*\n", "\n", "The `PP` also has some strange relations. We see what's going on with the same kind of inspection. First we look at the `rec` (regens) relations." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.87s 12 results\n", "12 results\n" ] }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *1*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "701824\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 701824 Adju PP\n", "
\n", "
\n", "\n", "
\n", "87947\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "87948\n", "
诇职旨
\n", "
prep to
\n", "\n", "\n", "
\n", "\n", "
\n", "87949\n", "
讘址讚蜘
\n", "
subs linen, part, stave
\n", "\n", "\n", "
\n", "\n", "
\n", "87950\n", "
注止诇址吱转
\n", "
subs burnt-offering
\n", "\n", "\n", "
\n", "\n", "
\n", "87951\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "87952\n", "
讞止譁讚侄砖讈
\n", "
subs month
\n", "\n", "\n", "
\n", "\n", "
\n", "87953\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "87954\n", "
诪执谞职讞指转指謼讛旨
\n", "
subs present
\n", "\n", "\n", "
\n", "\n", "
\n", "87955\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "87956\n", "
注止诇址证转
\n", "
subs burnt-offering
\n", "\n", "\n", "
\n", "\n", "
\n", "87957\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "87958\n", "
转指旨诪执讬讚謾
\n", "
subs continuity
\n", "\n", "\n", "
\n", "\n", "
\n", "87959\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "87960\n", "
诪执谞职讞指转指謹讛旨
\n", "
subs present
\n", "\n", "\n", "
\n", "\n", "
\n", "87961\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "87962\n", "
谞执住职讻值旨讬讛侄謻诐
\n", "
subs libation
\n", "\n", "\n", "
\n", "\n", "
\n", "87963\n", "
讻职旨
\n", "
prep as
\n", "\n", "\n", "
\n", "\n", "
\n", "87964\n", "
诪执砖职讈驻指旨讟指謶诐
\n", "
subs justice
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *2*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "729575\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 729575 Objc PP\n", "
\n", "
\n", "\n", "
\n", "138117\n", "
讗侄转志
\n", "
prep <object marker>
\n", "\n", "\n", "
\n", "\n", "
\n", "138118\n", "
讬址证讚
\n", "
subs hand
\n", "\n", "\n", "
\n", "\n", "
\n", "138119\n", "
讗址讞址讚謾
\n", "
subs one
\n", "\n", "\n", "
\n", "\n", "
\n", "138120\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "138121\n", "
讘指旨谞指謹讬讜
\n", "
subs son
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *3*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "763550\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 763550 PreC PP\n", "
\n", "
\n", "\n", "
\n", "193931\n", "
讻执旨
\n", "
prep as
\n", "\n", "\n", "
\n", "\n", "
\n", "193932\n", "
讚职讘址譀专
\n", "
subs word
\n", "\n", "\n", "
\n", "\n", "
\n", "193933\n", "
讗址讞址芝讚
\n", "
subs one
\n", "\n", "\n", "
\n", "\n", "
\n", "193934\n", "
诪值讛侄謻诐
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *4*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "793213\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 793213 Adju PP\n", "
\n", "
\n", "\n", "
\n", "240898\n", "
诪值
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "240899\n", "
专指注址郑转
\n", "
subs evil
\n", "\n", "\n", "
\n", "\n", "
\n", "240900\n", "
讬止纸砖职讈讘值讬志
\n", "
subs sit qal ptca
\n", "\n", "\n", "
\n", "\n", "
\n", "240901\n", "
讘指謼讛旨
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *5*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "848705\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 848705 Adju PP\n", "
\n", "
\n", "\n", "
\n", "329764\n", "
诪值譂
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "329765\n", "
专指注址謼转
\n", "
subs evil
\n", "\n", "\n", "
\n", "\n", "
\n", "329766\n", "
讬止郑砖职讈讘值讬
\n", "
subs sit qal ptca
\n", "\n", "\n", "
\n", "\n", "
\n", "329767\n", "
讘指纸讛旨變
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *6*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "893491\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 893491 Adju PP\n", "
\n", "
\n", "\n", "
\n", "404050\n", "
诇职
\n", "
prep to
\n", "\n", "\n", "
\n", "\n", "
\n", "404051\n", "
注只诪址旨转謾
\n", "
subs side
\n", "\n", "\n", "
\n", "\n", "
\n", "404052\n", "
讻址旨
\n", "
prep as
\n", "\n", "\n", "
\n", "\n", "
\n", "404053\n", "
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "404054\n", "
拽指旨讟止郑谉
\n", "
subs small
\n", "\n", "\n", "
\n", "\n", "
\n", "404055\n", "
讻址旨
\n", "
prep as
\n", "\n", "\n", "
\n", "\n", "
\n", "404056\n", "
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "404057\n", "
讙指旨讚止謹讜诇
\n", "
subs great
\n", "\n", "\n", "
\n", "\n", "
\n", "404058\n", "
诪值讘执謻讬谉
\n", "
subs understand hif ptca
\n", "\n", "\n", "
\n", "\n", "
\n", "404059\n", "
注执诐志
\n", "
prep with
\n", "\n", "\n", "
\n", "\n", "
\n", "404060\n", "
转址旨诇职诪执纸讬讚變 驻
\n", "
subs scholar
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rec_preps = \"\"\"\n", "\n", "phrase_atom typ=PP\n", " subphrase rela=rec\n", " word pdp=prep\n", "\"\"\"\n", "\n", "find_and_show(rec_preps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The PP is different. In cases where the `phrase_atom` = `rec`, the preposition is *not* the head. Thus, the algorithm will need to check for these cases.\n", "\n", "Now for the `adj` subphrase relation in `PP`:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.88s 42 results\n", "42 results\n" ] }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *1*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "659013\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 659013 Cmpl PP\n", "
\n", "
\n", "\n", "
\n", "12691\n", "
诪值纸
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "12692\n", "
讞植讜执讬诇指譁讛
\n", "
nmpr Havilah
\n", "\n", "\n", "
\n", "\n", "
\n", "12693\n", "
注址讚志
\n", "
prep unto
\n", "\n", "\n", "
\n", "\n", "
\n", "12694\n", "
砖讈謼讜旨专
\n", "
nmpr Shur
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *2*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "674965\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 674965 Time PP\n", "
\n", "
\n", "\n", "
\n", "37803\n", "
讘址旨
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "37804\n", "
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "37805\n", "
讘止旨郑拽侄专
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "
\n", "37806\n", "
讘址旨
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "37807\n", "
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "37808\n", "
讘止旨謹拽侄专
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *3*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "675546\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 675546 Time PP\n", "
\n", "
\n", "\n", "
\n", "38736\n", "
诪执谉志
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "38737\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "38738\n", "
讘止旨謻拽侄专
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "
\n", "38739\n", "
注址讚志
\n", "
prep unto
\n", "\n", "\n", "
\n", "\n", "
\n", "38740\n", "
讛指
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "38741\n", "
注指纸专侄讘變
\n", "
subs evening
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *4*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "675571\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 675571 Time PP\n", "
\n", "
\n", "\n", "
\n", "38778\n", "
诪执谉志
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "38779\n", "
讘止旨芝拽侄专
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "
\n", "38780\n", "
注址讚志
\n", "
prep unto
\n", "\n", "\n", "
\n", "\n", "
\n", "38781\n", "
注指纸专侄讘變
\n", "
subs evening
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *5*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "676757\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 676757 Subj NP\n", "
\n", "
\n", "\n", "
\n", "40647\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "40648\n", "
讙职旨谞值讘指謼讛
\n", "
subs what is stolen
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase 676757 Subj NP\n", "
\n", "
\n", "\n", "
\n", "40649\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "40650\n", "
砖止旨讈支讜专
\n", "
subs bullock
\n", "\n", "\n", "
\n", "\n", "
\n", "40651\n", "
注址讚志
\n", "
prep unto
\n", "\n", "\n", "
\n", "\n", "
\n", "40652\n", "
讞植诪止譀讜专
\n", "
subs he-ass
\n", "\n", "\n", "
\n", "\n", "
\n", "40653\n", "
注址讚志
\n", "
prep unto
\n", "\n", "\n", "
\n", "\n", "
\n", "40654\n", "
砖侄讉謻讛
\n", "
subs lamb
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *6*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "677725\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 677725 Adju PP\n", "
\n", "
\n", "\n", "
\n", "42226\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "42227\n", "
拽指旨爪指讛謾
\n", "
subs end
\n", "\n", "\n", "
\n", "\n", "
\n", "42228\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "42229\n", "
讝侄旨謹讛
\n", "
prde this
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *7*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "677728\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 677728 Adju PP\n", "
\n", "
\n", "\n", "
\n", "42233\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "42234\n", "
拽指旨爪指謻讛
\n", "
subs end
\n", "\n", "\n", "
\n", "\n", "
\n", "42235\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "42236\n", "
讝侄旨謶讛
\n", "
prde this
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *8*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "678437\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 678437 Time PP\n", "
\n", "
\n", "\n", "
\n", "43705\n", "
诪值
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "43706\n", "
注侄芝专侄讘
\n", "
subs evening
\n", "\n", "\n", "
\n", "\n", "
\n", "43707\n", "
注址讚志
\n", "
prep unto
\n", "\n", "\n", "
\n", "\n", "
\n", "43708\n", "
讘止旨謻拽侄专
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *9*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "679353\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 679353 Time PP\n", "
\n", "
\n", "\n", "
\n", "45628\n", "
讘址旨
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "45629\n", "
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "45630\n", "
讘止旨郑拽侄专
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "
\n", "45631\n", "
讘址旨
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "45632\n", "
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "45633\n", "
讘止旨謼拽侄专
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *10*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "681309\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 681309 Time PP\n", "
\n", "
\n", "\n", "
\n", "49254\n", "
讘址旨
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "49255\n", "
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "49256\n", "
讘止旨芝拽侄专
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "
\n", "49257\n", "
讘址旨
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "49258\n", "
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "49259\n", "
讘止旨纸拽侄专變
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *11*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "684131\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 684131 Time PP\n", "
\n", "
\n", "\n", "
\n", "55004\n", "
讘址旨
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "55005\n", "
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "55006\n", "
讘止旨郑拽侄专
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "
\n", "55007\n", "
讘址旨
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "55008\n", "
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "55009\n", "
讘止旨謶拽侄专
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *12*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "686147\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 686147 Cmpl PP\n", "
\n", "
\n", "\n", "
\n", "58901\n", "
讗侄诇志
\n", "
prep to
\n", "\n", "\n", "
\n", "\n", "
\n", "58902\n", "
讗址讛植专止郑谉
\n", "
nmpr Aaron
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase 686147 Cmpl PP\n", "
\n", "
\n", "\n", "
\n", "58903\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "58904\n", "
讻止旨讛值謹谉
\n", "
subs priest
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase 686147 Cmpl PP\n", "
\n", "
\n", "\n", "
\n", "58905\n", "
讗止譀讜
\n", "
conj or
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase 686147 Cmpl PP\n", "
\n", "
\n", "\n", "
\n", "58906\n", "
讗侄诇志
\n", "
prep to
\n", "\n", "\n", "
\n", "\n", "
\n", "58907\n", "
讗址讞址芝讚
\n", "
subs one
\n", "\n", "\n", "
\n", "\n", "
\n", "58908\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "58909\n", "
讘指旨谞指謻讬讜
\n", "
subs son
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase 686147 Cmpl PP\n", "
\n", "
\n", "\n", "
\n", "58910\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "58911\n", "
讻止旨讛植谞执纸讬诐變
\n", "
subs priest
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *13*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "695140\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 695140 PreC PP\n", "
\n", "
\n", "\n", "
\n", "76198\n", "
诪值
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "76199\n", "
注侄郑专侄讘
\n", "
subs evening
\n", "\n", "\n", "
\n", "\n", "
\n", "76200\n", "
注址讚志
\n", "
prep unto
\n", "\n", "\n", "
\n", "\n", "
\n", "76201\n", "
讘止旨謹拽侄专
\n", "
subs morning
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *14*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "701824\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 701824 Adju PP\n", "
\n", "
\n", "\n", "
\n", "87947\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "87948\n", "
诇职旨
\n", "
prep to
\n", "\n", "\n", "
\n", "\n", "
\n", "87949\n", "
讘址讚蜘
\n", "
subs linen, part, stave
\n", "\n", "\n", "
\n", "\n", "
\n", "87950\n", "
注止诇址吱转
\n", "
subs burnt-offering
\n", "\n", "\n", "
\n", "\n", "
\n", "87951\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "87952\n", "
讞止譁讚侄砖讈
\n", "
subs month
\n", "\n", "\n", "
\n", "\n", "
\n", "87953\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "87954\n", "
诪执谞职讞指转指謼讛旨
\n", "
subs present
\n", "\n", "\n", "
\n", "\n", "
\n", "87955\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "87956\n", "
注止诇址证转
\n", "
subs burnt-offering
\n", "\n", "\n", "
\n", "\n", "
\n", "87957\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "87958\n", "
转指旨诪执讬讚謾
\n", "
subs continuity
\n", "\n", "\n", "
\n", "\n", "
\n", "87959\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "87960\n", "
诪执谞职讞指转指謹讛旨
\n", "
subs present
\n", "\n", "\n", "
\n", "\n", "
\n", "87961\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "87962\n", "
谞执住职讻值旨讬讛侄謻诐
\n", "
subs libation
\n", "\n", "\n", "
\n", "\n", "
\n", "87963\n", "
讻职旨
\n", "
prep as
\n", "\n", "\n", "
\n", "\n", "
\n", "87964\n", "
诪执砖职讈驻指旨讟指謶诐
\n", "
subs justice
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *15*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "702443\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 702443 Objc NP\n", "
\n", "
\n", "\n", "
\n", "89462\n", "
讗侄讞指郑讚讇
\n", "
subs one
\n", "\n", "\n", "
\n", "\n", "
\n", "89463\n", "
讗指讞只郑讝
\n", "
subs seize qal ptcp
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase 702443 Objc NP\n", "
\n", "
\n", "\n", "
\n", "89464\n", "
诪执谉志
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "89465\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "89466\n", "
讞植诪执砖执旨讈謼讬诐
\n", "
subs five
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase 702443 Objc NP\n", "
\n", "
\n", "\n", "
\n", "89467\n", "
诪执谉志
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "89468\n", "
讛指
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "89469\n", "
讗指讚指支诐
\n", "
subs human, mankind
\n", "\n", "\n", "
\n", "\n", "
\n", "89470\n", "
诪执谉志
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "89471\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "89472\n", "
讘指旨拽指譀专
\n", "
subs cattle
\n", "\n", "\n", "
\n", "\n", "
\n", "89473\n", "
诪执谉志
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "89474\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "89475\n", "
讞植诪止专执芝讬诐
\n", "
subs he-ass
\n", "\n", "\n", "
\n", "\n", "
\n", "89476\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "89477\n", "
诪执谉志
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "89478\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "89479\n", "
爪止旨謻讗谉
\n", "
subs cattle
\n", "\n", "\n", "
\n", "\n", "
\n", "89480\n", "
诪执
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "89481\n", "
讻指旨诇志
\n", "
subs whole
\n", "\n", "\n", "
\n", "\n", "
\n", "89482\n", "
讛址
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "89483\n", "
讘职旨讛值诪指謶讛
\n", "
subs cattle
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *16*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "705970\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 705970 Cmpl PP\n", "
\n", "
\n", "\n", "
\n", "96035\n", "
讘职旨
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "96036\n", "
讝址专职注止謻讜
\n", "
subs seed
\n", "\n", "\n", "
\n", "\n", "
\n", "96037\n", "
讗址讞植专指謶讬讜
\n", "
prep after
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *17*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "706038\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 706038 Cmpl PP\n", "
\n", "
\n", "\n", "
\n", "96162\n", "
讗侄诇志
\n", "
prep to
\n", "\n", "\n", "
\n", "\n", "
\n", "96163\n", "
讗址讞址譀转
\n", "
subs one
\n", "\n", "\n", "
\n", "\n", "
\n", "96164\n", "
诪执谉志
\n", "
prep from
\n", "\n", "\n", "
\n", "\n", "
\n", "96165\n", "
讛侄
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "96166\n", "
注指专执芝讬诐
\n", "
subs town
\n", "\n", "\n", "
\n", "\n", "
\n", "96167\n", "
讛指
\n", "
art the
\n", "\n", "\n", "
\n", "\n", "
\n", "96168\n", "
讗值謻诇
\n", "
prde these
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *18*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "709891\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 709891 Adju PP\n", "
\n", "
\n", "\n", "
\n", "103115\n", "
讘值旨纸讬谉志
\n", "
prep interval
\n", "\n", "\n", "
\n", "\n", "
\n", "103116\n", "
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\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "adj_preps = \"\"\"\n", "\n", "phrase_atom typ=PP\n", " subphrase rela=adj\n", " word pdp=prep\n", "\"\"\"\n", "\n", "find_and_show(adj_preps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results above show that the `adj` subphrase relation is also a non-head. These cases have to be excluded.\n", "\n", "Now we move on to test the **adverb** relations reflected in the survey..." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.76s 1 result\n", "1 results\n" ] }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *1*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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讙址诐志
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讗指诪职谞指謼讛
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讙址旨诐志
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专址譀拽
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advb only
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讛址专职讞值芝拽
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\n", "125428\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "125429\n", "
讘职
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "125430\n", "
讝指讛指譁讘
\n", "
subs gold
\n", "\n", "\n", "
\n", "\n", "
\n", "125431\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "125432\n", "
讘执
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "125433\n", "
谞职讞止支砖侄讈转
\n", "
subs bronze
\n", "\n", "\n", "
\n", "\n", "
\n", "125434\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "125435\n", "
讘职
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "125436\n", "
讘址专职讝侄譀诇
\n", "
subs iron
\n", "\n", "\n", "
\n", "\n", "
\n", "125437\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "125438\n", "
讘执
\n", "
prep in
\n", "\n", "\n", "
\n", "\n", "
\n", "125439\n", "
砖职讉诇指诪止謻讜转
\n", "
subs wrapper
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase 721834 Adju PP\n", "
\n", "
\n", "\n", "
\n", "125440\n", "
讛址专职讘值旨郑讛
\n", "
advb be many hif infa
\n", "\n", "\n", "
\n", "\n", "
\n", "125441\n", "
诪职讗止謶讚
\n", "
advb might
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *16*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "731154\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 731154 Modi AdvP\n", "
\n", "
\n", "\n", "
\n", "140793\n", "
讗址讱职蜘
\n", "
advb only
\n", "\n", "\n", "
\n", "\n", "
\n", "140794\n", "
谞执讙止旨吱讜祝
\n", "
advb hurt nif infa
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *17*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "735506\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 735506 Time AdvP\n", "
\n", "
\n", "\n", "
\n", "147570\n", "
讙址旨诐志
\n", "
advb even
\n", "\n", "\n", "
\n", "\n", "
\n", "147571\n", "
注址转指旨讛謾
\n", "
advb now
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *18*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "741485\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 741485 Time AdvP\n", "
\n", "
\n", "\n", "
\n", "156845\n", "
讙址旨诐志
\n", "
advb even
\n", "\n", "\n", "
\n", "\n", "
\n", "156846\n", "
诇址謻讬职诇指讛
\n", "
advb night
\n", "\n", "\n", "
\n", "\n", "
\n", "156847\n", "
讙址旨诐志
\n", "
advb even
\n", "\n", "\n", "
\n", "\n", "
\n", "156848\n", "
讬止讜诪指謶诐
\n", "
advb by day
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "
\n", " phrase 741485 Time AdvP\n", "
\n", "
\n", "\n", "
\n", "156849\n", "
讻指旨诇志
\n", "
subs whole
\n", "\n", "\n", "
\n", "\n", "
\n", "156850\n", "
讬职诪值譀讬
\n", "
subs day
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *19*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "744756\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 744756 Time AdvP\n", "
\n", "
\n", "\n", "
\n", "162031\n", "
讙址旨诐志
\n", "
advb even
\n", "\n", "\n", "
\n", "\n", "
\n", "162032\n", "
转职旨诪止讜诇謾
\n", "
advb yesterday
\n", "\n", "\n", "
\n", "\n", "
\n", "162033\n", "
讙址旨诐志
\n", "
advb even
\n", "\n", "\n", "
\n", "\n", "
\n", "162034\n", "
砖执讈诇职砖止讈謹诐
\n", "
advb day before yesterday
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *20*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "745299\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 745299 Time AdvP\n", "
\n", "
\n", "\n", "
\n", "162945\n", "
讙址旨诐志
\n", "
advb even
\n", "\n", "\n", "
\n", "\n", "
\n", "162946\n", "
讗侄转职诪止郑讜诇
\n", "
advb yesterday
\n", "\n", "\n", "
\n", "\n", "
\n", "162947\n", "
讙址旨诐志
\n", "
advb even
\n", "\n", "\n", "
\n", "\n", "
\n", "162948\n", "
砖执讈诇职砖止讈謼讜诐
\n", "
advb day before yesterday
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "adv_mod = \"\"\"\n", "\n", "phrase_atom typ=AdvP\n", " subphrase rela=mod\n", " word pdp=advb\n", "\n", "\"\"\"\n", "\n", "find_and_show(adv_mod)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, it appears that `mod` is also an invalid relation for adverb phrases. And example is 讙诐 讛诇诐 ('also here') where 讙诐 is the adverb in `mod` relation, but the head is really 讛诇诐 \"here\" (also an adverb). In several cases, the modifier modifies a verb. In these cases the \"head,\" often a participle or infinitive, acts as the adverb, even though it is not explicitly marked as such.\n", "\n", "Now we move on to the last examination, that of the `AdjP` (adjective phrase). There are three relations of interest:\n", "> `atr` - 6
\n", "> `adj` - 3
\n", "> `rec` - 1
" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.77s 5 results\n", "5 results\n" ] }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *1*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "661695\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 661695 PreC AdjP\n", "
\n", "
\n", "\n", "
\n", "16552\n", "
注植拽只讚执旨芝讬诐
\n", "
adjv twisted
\n", "\n", "\n", "
\n", "\n", "
\n", "16553\n", "
谞职拽只讚执旨謻讬诐
\n", "
adjv speckled
\n", "\n", "\n", "
\n", "\n", "
\n", "16554\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "16555\n", "
讘职专只讚执旨纸讬诐變
\n", "
adjv speckled
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *2*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "661716\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 661716 PreC AdjP\n", "
\n", "
\n", "\n", "
\n", "16584\n", "
注植拽只讚执旨芝讬诐
\n", "
adjv twisted
\n", "\n", "\n", "
\n", "\n", "
\n", "16585\n", "
谞职拽只讚执旨謻讬诐
\n", "
adjv speckled
\n", "\n", "\n", "
\n", "\n", "
\n", "16586\n", "
讜旨
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "16587\n", "
讘职专只讚执旨謶讬诐
\n", "
adjv speckled
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *3*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "715006\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 715006 PreC AdjP\n", "
\n", "
\n", "\n", "
\n", "111758\n", "
诇职讞只芝诪值讬
\n", "
adjv feed qal ptcp
\n", "\n", "\n", "
\n", "\n", "
\n", "111759\n", "
专侄謻砖侄讈祝
\n", "
subs flame
\n", "\n", "\n", "
\n", "\n", "
\n", "111760\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "111761\n", "
拽侄郑讟侄讘
\n", "
subs sting
\n", "\n", "\n", "
\n", "\n", "
\n", "111762\n", "
诪职专执讬专执謶讬
\n", "
adjv bitter
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "adj_atr = \"\"\"\n", "\n", "phrase_atom typ=AdjP\n", " subphrase rela=atr\n", " word pdp=adjv\n", "\n", "\"\"\"\n", "\n", "find_and_show(adj_atr)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.78s 3 results\n", "3 results\n" ] }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *1*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "686718\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 686718 PreC AdjP\n", "
\n", "
\n", "\n", "
\n", "59788\n", "
诇职讘指谞指郑讛
\n", "
adjv white
\n", "\n", "\n", "
\n", "\n", "
\n", "59789\n", "
讗植讚址诪职讚侄旨謹诪侄转
\n", "
adjv reddish
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *2*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "853166\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 853166 PreC AdjP\n", "
\n", "
\n", "\n", "
\n", "336212\n", "
转指旨支诐
\n", "
adjv complete
\n", "\n", "\n", "
\n", "\n", "
\n", "336213\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "336214\n", "
讬指砖指讈譀专
\n", "
adjv right
\n", "\n", "\n", "
\n", "\n", "
\n", "336215\n", "
讬职专值芝讗
\n", "
adjv afraid
\n", "\n", "\n", "
\n", "\n", "
\n", "336216\n", "
讗直诇止讛执謻讬诐
\n", "
subs god(s)
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *3*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "853425\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 853425 PreC AdjP\n", "
\n", "
\n", "\n", "
\n", "336595\n", "
转指旨支诐
\n", "
adjv complete
\n", "\n", "\n", "
\n", "\n", "
\n", "336596\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "336597\n", "
讬指砖指讈譀专
\n", "
adjv right
\n", "\n", "\n", "
\n", "\n", "
\n", "336598\n", "
讬职专值芝讗
\n", "
adjv afraid
\n", "\n", "\n", "
\n", "\n", "
\n", "336599\n", "
讗直诇止讛执謻讬诐
\n", "
subs god(s)
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "adj_adj = \"\"\"\n", "\n", "phrase_atom typ=AdjP\n", " subphrase rela=adj\n", " word pdp=adjv\n", "\n", "\"\"\"\n", "\n", "find_and_show(adj_adj)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.83s 1 result\n", "1 results\n" ] }, { "data": { "text/markdown": [ "\n", "\n", "**phrase** *1*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "715006\n", "\n", "\n", "\n", "
\n", "\n", "
\n", " phrase 715006 PreC AdjP\n", "
\n", "
\n", "\n", "
\n", "111758\n", "
诇职讞只芝诪值讬
\n", "
adjv feed qal ptcp
\n", "\n", "\n", "
\n", "\n", "
\n", "111759\n", "
专侄謻砖侄讈祝
\n", "
subs flame
\n", "\n", "\n", "
\n", "\n", "
\n", "111760\n", "
讜职
\n", "
conj and
\n", "\n", "\n", "
\n", "\n", "
\n", "111761\n", "
拽侄郑讟侄讘
\n", "
subs sting
\n", "\n", "\n", "
\n", "\n", "
\n", "111762\n", "
诪职专执讬专执謶讬
\n", "
adjv bitter
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "adj_rec = \"\"\"\n", "\n", "phrase_atom typ=AdjP\n", " subphrase rela=rec\n", " word pdp=adjv\n", "\n", "\"\"\"\n", "\n", "find_and_show(adj_rec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results for the three searches above show indeed that the relations of `atr`, `adj`, and `rec` are not head words." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tests for phrase types without a word that has a valid `pdp` value\n", "\n", "The initial survey above revealed that 837 phrase atoms and 670 phrases lack a word with a corresponding `pdp` value. Here we investigate to see why that is the case. Is there a way to compensate for this problem? Are these truly phrases that lack heads?\n", "\n", "We run another survey and count the phrase types against the non-matching `pdp` values found within them. At this point, we must also exclude words that have dependent relations (as defined above, subphrase values of NA or parallel)." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AdvP\n", "\t nmpr - 253\n", "\t subs - 499\n", "\t art - 190\n", "\t conj - 13\n", "PrNP\n", "\t subs - 9\n", "\t art - 3\n", "CP\n", "\t prep - 85\n", "\t subs - 79\n", "\t advb - 6\n", "NP\n", "\t intj - 1\n" ] } ], "source": [ "count_no_pdp = collections.defaultdict(lambda: collections.Counter())\n", "record_no_pdp = collections.defaultdict(lambda: collections.defaultdict(list))\n", "\n", "for phrase in F.otype.s(\"phrase_atom\"):\n", "\n", " typ = F.typ.v(phrase)\n", "\n", " # see if there is not corresponding `pdp` value\n", " corres_pdp = type_to_pdp[typ]\n", " corresponding_pdps = [w for w in L.d(phrase, \"word\") if F.pdp.v(w) == corres_pdp]\n", "\n", " if not corresponding_pdps:\n", "\n", " # put potential heads here\n", " maybe_heads = []\n", "\n", " # calculate subphrase relations\n", " for word in L.d(phrase, \"word\"):\n", "\n", " # get subphrase relations\n", " word_subphrs = L.u(word, \"subphrase\")\n", " sp_relas = set(F.rela.v(sp) for sp in word_subphrs) or {\"NA\"}\n", "\n", " # check subphrase relations for independence\n", " if sp_relas == {\"NA\"}:\n", " maybe_heads.append(word)\n", "\n", " # test parallel relation for independence\n", " elif sp_relas == {\"NA\", \"par\"} or sp_relas == {\"par\"}:\n", "\n", " # check for good, head mothers\n", " good_mothers = set(\n", " sp for w in maybe_heads for sp in L.u(w, \"subphrase\")\n", " )\n", " this_daughter = [sp for sp in word_subphrs if F.rela.v(sp) == \"par\"][0]\n", " this_mother = E.mother.f(this_daughter)\n", "\n", " if this_mother in good_mothers:\n", " maybe_heads.append(word)\n", "\n", " # sanity check\n", " # maybe_heads should have SOMETHING\n", " if not maybe_heads:\n", " raise Exception(f\"phrase {phrase} looks HEADLESS!\")\n", "\n", " # count pdp types\n", " head_pdps = [F.pdp.v(w) for w in maybe_heads]\n", " count_no_pdp[typ].update(head_pdps)\n", "\n", " # save for examination\n", " for word in maybe_heads:\n", " record_no_pdp[typ][F.pdp.v(word)].append((phrase, word))\n", "\n", "for name, counts in count_no_pdp.items():\n", "\n", " print(name)\n", "\n", " for pdp, count in counts.items():\n", " print(\"\\t\", pdp, \"-\", count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These results are a bit puzzling. The numbers here are words within the phrase atoms that have NO subphrase relations. That means, for example, words such as 讛址 \"the\" do not appear to have any subphrase relation to their modified nouns. That again illustrates the shortcoming of the ETCBC data in this respect. There should be a relation from the article to the determined noun.\n", "\n", "From this point forward, I will begin working through all four phrase types and the cases reflected in the survey.\n", "\n", "Beginning with the `AdvP` type and the article. Upon some initial inspection, I've found that in many of the `AdvP` with the article, there is also a substantive (`subs`) that was found by the search. Are there any cases where there is no `nmpr` or `subs` found alongside the article? We can use the dict `record_no_pdp` which has recorded all cases reflected in the survey. Below I look to see if all 190 cases of an article in these `AdvP` phrases also has a corresponding noun." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 without nouns found...\n" ] } ], "source": [ "no_noun = []\n", "\n", "for phrase in record_no_pdp[\"AdvP\"][\"art\"]:\n", "\n", " pdps = set(F.pdp.v(w) for w in L.d(phrase[0], \"word\"))\n", "\n", " if not {\"nmpr\", \"subs\"} & pdps:\n", " no_noun.append((phrase,))\n", "\n", "print(len(no_noun), \"without nouns found...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There it is. So all cases of these articles can be discarded. In these cases, the noun serves as the head of the adverbial phrase. An example of this is when the noun marks the location of the action (hence adverb).\n", "\n", "Next, we check the conjunctions found in the adverbial phrases. Are any of those heads?" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# B.show(record_no_pdp['AdvP']['conj']) # uncomment me!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All conjunctions in these `AdvP` phrases function to mark coordinate elements (only 讜 in these results). They can also be discarded as not possible heads.\n", "\n", "Now we investigate the `PrNP` results with `subs` and `art`..." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# B.show(record_no_pdp['PrNP']['subs']) # uncomment me!" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "# B.show(record_no_pdp['PrNP']['art']) # uncomment me!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `art` relations reflected in the second search are not heads, but are all related to a substantive. All of the results in `subs` are heads. Thus, the only acceptable `pdp` for `PrNP` besides a proper noun is `subs`.\n", "\n", "Now we dig into `CP` results. 85 of them have no `pdp` of conjunction, but have a preposition instead. Let's see what's going on..." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "# B.show(record_no_pdp['CP']['prep'][:20]) # uncomment me!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are very interesting results. These conjunction phrases are made up of constructions like 讘+注讘讜专 and 讘+讟专诐. Together these words function as a conjunction, but alone they are prepositions and particles. Is it even possible in this case to say that there is a \"head\"?\n", "\n", "It could be said that these combinations of words mean more than the sum of their parts; they are good examples of constructions, i.e. combinations of words whose meaning cannot be inferred simply from their individual words. Constructions illustrate the vague boundary between syntax and lexicon (cf. e.g. Goldberg, 1995, *Constructions*).\n", "\n", "While these words are indeed marked as conjunction phrases, it is better in this case to analyse them as prepositional phrases (which they also are...this is another shortcoming of our data, or perhaps a mistake??). Thus, the head is the preposition, not the prepositional object.\n", "\n", "We should expect that the remaining `subs` and `advb` groups are in fact the objects of those prepositions (and hence excluded). Let's test that assumption by looking for a preposition behind these words..." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "subs|advb with no preceding prepositions: 0\n" ] } ], "source": [ "no_prep = []\n", "\n", "for (phrase, word) in record_no_pdp[\"CP\"][\"subs\"] + record_no_pdp[\"CP\"][\"advb\"]:\n", "\n", " possible_prep = word - 1\n", "\n", " if F.sp.v(possible_prep) != \"prep\":\n", " no_prep.append((phrase, word))\n", "\n", "print(f\"subs|advb with no preceding prepositions: {len(no_prep)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we see. We can confirm that none of the nouns or adverbs will be the head of a conjunction phrase. A preposition is the only other kind of head for the `CP` besides a conjunction itself.\n", "\n", "Finally, we're left with a last noun phrase (`NP`) for which no matching noun was found. The search found instead both `adjv` (adjective) and a `intj` (interjection). Let's see it." ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "# B.show(record_no_pdp['NP']['intj']) # uncomment me" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, the word 讗讜讬 \"woe\" functions like a noun. This thus appears to be another mislabelled `pdp` value, since it should read `subs`. This, like the previous example, will not receive a head value due to the mistake." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Retrieving Quantified Words\n", "\n", "When the heads algorithm looks for a noun without any subphrase relations in the phrase, it will often return a quantifier noun such as a number, e.g. 砖讘注讛 \"seven\", or such as another descriptor like 讻诇. But these words function semantically in a more descriptive role than a head role. Thus, we want our algorithm to isolate quantified nouns from their quantifiers. To do that means we must first know how the ETCBC encodes the relationship between a quantifier and the quantified noun.\n", "\n", "In a previous algorithm used for quantified extraction, we looked for a nomen regens relation on the quantifier and located the noun within the related subphrase. This approach works well for the quantifier 讻诇. But for cardinal numbers, the relation `adj` (adjunct) is often used as well (as seen in the surveys below).\n", "\n", "To illustrate with the search below, the quantifier 砖讘注讛 \"seven\" has no nomen regens relation:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "# B.show([(2217,)]) # uncomment me!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rather than reflecting a regen/rectum relation, the second word 砖谞讬诐 \"years,\" the quantified noun, has a subphrase relation of `adj` \"adjunct\":" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "砖侄讈郑讘址注 砖指讈谞执謹讬诐 讜旨砖职讈诪止谞侄芝讛 诪值讗止謻讜转 砖指讈谞指謶讛 \n", "\n", "1301096 (subphrase)\n", "\t 砖指讈谞执謹讬诐 \n", "\t rela: adj\n", "\n", "1301097 (subphrase)\n", "\t 砖侄讈郑讘址注 砖指讈谞执謹讬诐 \n", "\t rela: NA\n", "\n" ] } ], "source": [ "print(T.text(L.d(652883, \"word\")))\n", "print()\n", "\n", "for sp in L.u(2218, \"subphrase\"): # subphrases belonging to \"years\"\n", " print(sp, \"(subphrase)\")\n", " print(\"\\t\", T.text(L.d(sp, \"word\")))\n", " print(\"\\t\", \"rela:\", F.rela.v(sp))\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what other kinds of subphrase relations are reflected by quantifieds.\n", "\n", "Below we make a survey of all mother-daughter relations between a quantifier subphrase and its daughters. The goal is to isolate those relationships which contain the quantified noun. We work through examples to get an idea of the meaning of the features. And we write a few TF search queries further below to confirm hypotheses about these relationships." ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">XD/\n", "\t par - 62\n", "\t adj - 42\n", "\t rec - 69\n", "\t Appo - 1\n", "\t Spec - 6\n", "\t atr - 8\n", "\t mod - 4\n", "CNJM/\n", "\t rec - 345\n", "\t adj - 267\n", "\t par - 109\n", "\t mod - 14\n", "\t atr - 5\n", "\t Spec - 6\n", "\t dem - 3\n", "\t Sfxs - 1\n", ">RBH/\n", "\t rec - 30\n", "\t adj - 302\n", "\t par - 217\n", "\t atr - 2\n", "CMNH/\n", "\t adj - 128\n", "\t par - 28\n", "\t rec - 8\n", "\t mod - 1\n", "TCLP=/\n", "\t adj - 143\n", "\t rec - 33\n", "\t par - 198\n", "\t Spec - 1\n", "\t atr - 2\n", "/\n", "\t adj - 3\n", "\t par - 3\n", "XD/\n", "\t atr - 1\n", "\t Spec - 2\n", "CTJN/\n", "\t par - 1\n", "CT/\n", "TLT/\n", "\t adj - 2\n", "TRJN/\n", "\t rec - 2\n", ">LP/\n", "\t rec - 1\n", "XD/']['atr']) # uncomment me!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Spec` (`phrase_atom` `rela`) are cases where a phrase atom is used to add adjectival information about the quantifier." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "# B.show(quant_ex['>XD/']['Spec']) # uncomment me!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `mod` relation are cases where the quantifier is modified with particles like 讙诐 or 专拽" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "# B.show(quant_ex['CNJM/']['mod']) # uncomment me!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `dem` relation is when a demonstrative like 讗诇讛 modifies the quantifier." ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "# B.show(quant_ex['CB Subs Missed Results\n", "\n", "In the first test, several nouns are missed due to the presence of an adjectival element. Let's look at those cases and see what's going on. I have copied the phrase numbers of a few relevant examples." ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "adj_examples = [(771933,), (799523,)]\n", "\n", "# B.show(adj_examples) # uncomment me" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "subphrase \ttext \trelation \tmother\n", "讘侄谉志 1355711 NA ()\n", "讗指诪止謹讜抓 1355712 rec (207817,)\n" ] } ], "source": [ "show_subphrases(adj_examples[0][0])" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "讬职砖址纸讈注职讬指郑讛讜旨 nmpr\n", "讘侄谉志 subs\n", "讗指诪止謹讜抓 nmpr\n" ] } ], "source": [ "for word in L.d(adj_examples[0][0], \"word\"):\n", " print(T.text([word]), F.pdp.v(word))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, the substantive is not detected by the algorithm since it is in a dependent subphrase, a construct relation, with its modifying adjective. How to extract these nouns?\n", "\n", "This is very similar to the quantifier case, where the word in the rectum is actually the head (e.g. 砖转讬 砖谞讛 \"two years\" where \"two\" is registered as the head, but the substantive \"years\" is the semantic head). This kind of relationship is differentiated from non-heads by the fact that the adjective itself is independent. Thus, in cases where the adjective is independent and has a daughter rectum subphrase, the algorithm should retrieve the attributed noun.\n", "\n", "**proposed solution**: Add `adjv` to the set of acceptable `pdp` for the `NP`. Any adjectives will be processed for dependency: most will fail that test. But for the dozens of cases where the adjective does not fail, the algorithm will apply a separate check for a `rec` related subphrase which contains the true head.\n", "\n", "### Participle -> Head Missed Results\n", "\n", "Other phrases that end up headless are noun phrases that have a participle which serves as a the nominal element, but since it has satellites is coded as a \"verb\":" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "verb_examples = [(709010,), (711593,), (756104,)]\n", "\n", "# B.show(verb_examples) # uncomment me" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "subphrase \ttext \trelation \tmother\n", "\n", "subphrase \ttext \trelation \tmother\n", "\n", "subphrase \ttext \trelation \tmother\n", "\n" ] } ], "source": [ "for phrase in verb_examples:\n", " show_subphrases(phrase[0])\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are mixed cases here due to the shortcomings of the current data model. In these cases, the participle is marked as a \"verb\" since it also has objects or descriptors. In the first example above, the noun 讙专讛 functions as the *object* of the verb. The head is 诪注诇讛. But the same logic does not hold for the second or third case. In the second case, 止 驻爪讜注志讚讻讗 gives an *attribute* or quality of 砖驻讻讛. In the third case, 诪爪拽 \"poured\", describes an attribute of 谞讞砖转 \"bronze.\" Thus the opposite of example 1 is true, that is, the head noun is the attributed noun in the construct relation.\n", "\n", "Since the specific role of the noun or the verb is not specified at this lower phrase level, is there even a way to differentiate these cases?\n", "\n" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "709010\n", "讜职 conj\n", "\n", "711593\n", "讗值芝讬谉 nega\n", "\n", "756104\n", "讘值讬转止讜蜘 subs\n", "\n" ] } ], "source": [ "for phrase in verb_examples:\n", " print(phrase[0])\n", " for word in L.d(phrase[0], \"word\"):\n", " print(T.text([word]), F.pdp.v(word))\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It actually appears that the database treats all 3 the same: as adjectives at the phrase-dependent part of speech level. Thus, these cases will receive the same treatments as the adjective cases above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `KL/` relation problems\n", "\n", "I found an instance in Number 3:15 where the subphrase relationship that connects 讻诇 with its quantified noun is `atr` .That is probably wrong. Are there other cases with the same problem?" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kl_prob = \"\"\"\n", "\n", "sp1:subphrase\n", " w1:word lex=KL/ st=a\n", "\n", "sp2:subphrase rela=atr\n", "\n", "sp2 -mother> sp1\n", "sp2 >> sp1\n", "w1 :: sp1\n", "\"\"\"\n", "\n", "kl_prob = sorted(B.search(kl_prob))\n", "\n", "len(kl_prob)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [], "source": [ "# B.show(kl_prob) # uncomment me" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems that the adjectives are not nominalised in this construction as `pdp` of `subs`. Most of the findings are adjectives in construct with 讻诇. But there are several cases of the participle also.\n", "\n", "Is this encoding correct?\n", "\n", "If the `rela` code were properly `rec` as most are, then this would simply be a matter of adding an additional acceptable `pdp` to the list within the `get_quantified` function." ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# kl_prob = [r for r in kl_prob if not {'adjv'} and set(F.pdp.v(w) for w in L.d(r[2]))]\n", "\n", "len(kl_prob)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kl_prob = set(r[0] for r in kl_prob)\n", "\n", "len(kl_prob)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Subphrase by Subphrase Approach?\n", "\n", "Experimenting with switching from a word-by-word approach to a subphrase-by-subphrase. The first iteration of the `get_heads` function iterated word by word to identify valid heads with independent subphrase relations. A more efficient, and methodologically sound approach would be to work from the subphrase down to the word. Here I experiment with such a method." ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [], "source": [ "test_phrases = [\n", " ph\n", " for ph in F.typ.s(\"NP\")\n", " if len(L.d(ph, \"word\")) == 5 and F.otype.v(ph) == \"phrase\"\n", "]" ] }, { "cell_type": "code", "execution_count": 194, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "655731" ] }, "execution_count": 194, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test = test_phrases[20]\n", "\n", "test" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "subphrase \ttext \trelation \tmother\n", "讬职诇执芝讬讚 讘值旨纸讬转职讱指謻 1302879 NA ()\n", "讬职诇执芝讬讚 1302877 NA ()\n", "讘值旨纸讬转职讱指謻 1302878 rec (7530,)\n", "诪执拽职谞址郑转 讻址旨住职驻侄旨謶讱指 1302882 par (1302879,)\n", "诪执拽职谞址郑转 1302880 NA ()\n", "讻址旨住职驻侄旨謶讱指 1302881 rec (7533,)\n" ] } ], "source": [ "show_subphrases(test)" ] }, { "cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1302879, 1302877, 1302880]" ] }, "execution_count": 196, "metadata": {}, "output_type": "execute_result" } ], "source": [ "head_cands = [sp for sp in L.d(test, \"subphrase\") if F.rela.v(sp) == \"NA\"]\n", "\n", "head_cands" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note above that the heads are those within NA relations that consist of single words. How consistent is this? Are there any cases where the head does not receive its own individual subphrase with a NA relation? Or are there cases of NA relations of non-head elements? Below we run a couple of tests, and then we build a primitive head finder based on this hypothesis in order to manually inspect what happens." ] }, { "cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "example found: \n", "23 (1300568,) {'rec'}\n", "search complete with 0 results\n" ] } ], "source": [ "for word in F.otype.s(\"word\"):\n", "\n", " subphrases = L.u(word, \"subphrase\")\n", "\n", " if not subphrases:\n", " continue\n", "\n", " sp_relas = set(F.rela.v(sp) for sp in subphrases)\n", "\n", " if not {\"NA\", \"par\"} & sp_relas:\n", " print(\"example found: \")\n", " print(word, subphrases, sp_relas)\n", " break\n", "\n", "print(\"search complete with 0 results\")" ] }, { "cell_type": "code", "execution_count": 202, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'转职讛止謶讜诐 '" ] }, "execution_count": 202, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T.text(L.d(1300568, \"word\"))" ] }, { "cell_type": "code", "execution_count": 224, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "45161" ] }, "execution_count": 224, "metadata": {}, "output_type": "execute_result" } ], "source": [ "no_na = \"\"\"\n", "\n", "sp1:subphrase\n", " w1:word\n", "sp2:subphrase\n", "\n", "sp2 -mother> w1\n", "\"\"\"\n", "\n", "no_na = sorted(S.search(no_na))\n", "\n", "len(no_na)" ] }, { "cell_type": "code", "execution_count": 229, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "words with construct relation and no NA subphrase: 0\n" ] } ], "source": [ "no_na_filtered = []\n", "\n", "for r in no_na:\n", "\n", " reg = r[1]\n", "\n", " reg_subphrases = L.u(reg, \"subphrase\")\n", " reg_sp_relas = set(F.rela.v(sp) for sp in reg_subphrases)\n", "\n", " if \"NA\" not in reg_sp_relas:\n", " no_na_filtered.append(r)\n", "\n", "print(f\"words with construct relation and no NA subphrase: {len(no_na_filtered)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The search above shows that in any case that a word is in a construct relation with a subphrase, a NA (no relation) subphrase exists.\n", "\n", "Let's broaden the inquiry a bit. What are the specific situations in which there is NO non-related subphrase at all. What kinds of relations are present? What kinds of phrases are they?" ] }, { "cell_type": "code", "execution_count": 236, "metadata": { "lines_to_end_of_cell_marker": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({'NO subphrases': 215258, 'has NA': 37952})\n" ] } ], "source": [ "na_survey = collections.Counter()\n", "\n", "for phrase in F.otype.s(\"phrase\"):\n", "\n", " subphrase_relas = tuple(\n", " sorted(set(F.rela.v(sp) for sp in L.d(phrase, \"subphrase\")))\n", " )\n", "\n", " if not subphrase_relas:\n", " na_survey[\"NO subphrases\"] += 1\n", "\n", " elif \"NA\" in subphrase_relas:\n", " na_survey[\"has NA\"] += 1\n", "\n", " else:\n", " na_survey[subphrase_relas] += 1\n", "\n", "pprint(na_survey)" ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 2 }, "source": [ "This count shows that there are only two situations in the data: either\n", "\n", "1) a phrase has no subphrases present, or\n", "\n", "2) it has a subphrase with a relation of \"NA\". There are NO cases of phrases that lack an NA subphrase but have other relations. That is good for our hypothesis...\n", "\n", "In the experiment below, two important assumptions are made about the head:\n", "\n", "**First**, it is assumed that **the head is the first valid `pdp` word in the phrase**, with the exception of quantifieds and attributed nouns which are handled differently.\n", "\n", "**Second**, it is assumed that the **first NA-relation subphrase contains the head**. We test that assumption by manually inspecting the output." ] }, { "cell_type": "code", "execution_count": 292, "metadata": {}, "outputs": [], "source": [ "def primitive_head_hunter(phrase):\n", "\n", " \"\"\"\n", " Looks at noun phrases for heads.\n", " \"\"\"\n", "\n", " good_pdp = {\"subs\", \"nmpr\"}\n", "\n", " subphrase_candidates = [\n", " sp\n", " for sp in L.d(phrase, \"subphrase\")\n", " if F.rela.v(sp) == \"NA\" and F.rela.v(L.u(sp, \"phrase_atom\")[0]) == \"NA\"\n", " ]\n", "\n", " # handle simple phrases\n", " if not subphrase_candidates:\n", " head_candidates = [w for w in L.d(phrase, \"word\") if F.pdp.v(w) in good_pdp]\n", " try:\n", " return (head_candidates[0],)\n", " except:\n", " print(f\"exception at {phrase}\")\n", "\n", " # attempt simple head assignment\n", " first_na_subphrase = subphrase_candidates[0]\n", " try:\n", " the_head = next(\n", " w for w in L.d(first_na_subphrase, \"word\") if F.pdp.v(w) in good_pdp\n", " )\n", " return (the_head,)\n", " except:\n", " if F.pdp.v(L.d(first_na_subphrase, \"word\")[0]) == \"adjv\":\n", " pass\n", " else:\n", " raise Exception(phrase)" ] }, { "cell_type": "code", "execution_count": 296, "metadata": {}, "outputs": [], "source": [ "test_results = [primitive_head_hunter(ph) for ph in test_phrases]\n", "\n", "random.shuffle(test_results)" ] }, { "cell_type": "code", "execution_count": 298, "metadata": {}, "outputs": [], "source": [ "# B.show(test_results) # uncomment me" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As it turns out, the assumption about NA phrase type is workable. But the complications of this approach (explained below) make it an unlikely solution for now." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Conclusion\n", "\n", "I've done some initial testing with the subphrase by subphrase approach. It is a promising method, but requires a more complicated implementation with nested searches through each level of the phrase hierarchy. A simple subphrase by subphrase approach is not sufficient鈥攐ne needs to go phrase by phrase, `phrase_atom` by `phrase_atom`, `subphrase` by `subphrase`, and even beyond. It is a recursive problem that cannot be navigated with the present, limited data model. There is more to say about the present state of the data model which I will save for the final report.\n", "\n", "At present, the word-by-word approach provides an elegant (though limited) solution that is able to navigate the quirks of the present data model and provide an acceptable level of accuracy, with some exceptions for more complicated phrase constructions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Handling Parallels\n", "\n", "What is the best way to handle parallel head elements? In general, a phrase has only one real \"head\". That is, often the first head element determines the grammatical gender or number of the verb (thanks to Constantijn Sikkel for this conversation). Yet, the nouns which are coordinate to the head are often of interest for both grammatical and semantic studies.\n", "\n", "There are two approaches to collecting coordinate heads. One is to check for every word with a relation of \"parallel\" whether its mother is already established as a head. Another approach is to recursively search for nouns that are coordinate with the head word. Up until this inquiry, I have opted for option 1 due to the complexity of checking necessary relationships for a head candidate. But a phrase in Deuteronomy 12:17 is then missed by this current approach, since there is there a chain of head nouns in construct with the quantifier 诪注砖专 \"tenth\". These cases are missed.\n", "\n", "It is possible to edit the algorithm to accommodate these cases. But the example raises the broader question of whether option 1 is truly sufficient and methodologically sound. In this section, I test whether option 2 is a better alternative. First, we are unsure about how to separate a head word from a larger, paralleled subphrase. In option 1, individual words are tested, each for dependent relationships. But option 2 will go the opposite direction: beginning at the subphrase level and working down to the word. Does this affect our ability to separate the head noun of the phrase?" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "def OLD_get_heads(phrase):\n", " \"\"\"\n", " Extracts and returns the heads of a supplied\n", " phrase or phrase atom based on that phrase's type\n", " and the relations reflected within the phrase.\n", "\n", " --input--\n", " phrase(atom) node number\n", "\n", " --output--\n", " tuple of head word node(s)\n", " \"\"\"\n", "\n", " # mapping from phrase type to good part of speech values for heads\n", " head_pdps = {\n", " \"VP\": {\"verb\"}, # verb\n", " \"NP\": {\"subs\", \"adjv\", \"nmpr\"}, # noun\n", " \"PrNP\": {\"nmpr\", \"subs\"}, # proper-noun\n", " \"AdvP\": {\"advb\", \"nmpr\", \"subs\"}, # adverbial\n", " \"PP\": {\"prep\"}, # prepositional\n", " \"CP\": {\"conj\", \"prep\"}, # conjunctive\n", " \"PPrP\": {\"prps\"}, # personal pronoun\n", " \"DPrP\": {\"prde\"}, # demonstrative pronoun\n", " \"IPrP\": {\"prin\"}, # interrogative pronoun\n", " \"InjP\": {\"intj\"}, # interjectional\n", " \"NegP\": {\"nega\"}, # negative\n", " \"InrP\": {\"inrg\"}, # interrogative\n", " \"AdjP\": {\"adjv\"}, # adjective\n", " }\n", "\n", " # get phrase-head's part of speech value and list of candidate matches\n", " phrase_type = F.typ.v(phrase)\n", " head_candidates = [\n", " w for w in L.d(phrase, \"word\") if F.pdp.v(w) in head_pdps[phrase_type]\n", " ]\n", "\n", " # VP with verbs require no further processing, return the head verb\n", " if phrase_type == \"VP\":\n", " return tuple(head_candidates)\n", "\n", " # go head-hunting!\n", " heads = []\n", "\n", " for word in head_candidates:\n", "\n", " # gather the word's subphrase (+ phrase_atom if otype is phrase) relations\n", " word_phrases = list(L.u(word, \"subphrase\"))\n", " word_phrases += (\n", " list(L.u(word, \"phrase_atom\"))\n", " if (F.otype.v(phrase) == \"phrase\")\n", " else list()\n", " )\n", " word_relas = set(F.rela.v(phr) for phr in word_phrases) or {\"NA\"}\n", "\n", " # check (sub)phrase relations for independency\n", " if word_relas - {\"NA\", \"par\", \"Para\"}:\n", " continue\n", "\n", " # check parallel relations for independency\n", " elif word_relas & {\"par\", \"Para\"} and mother_is_head(word_phrases, heads):\n", " this_head = find_quantified(word) or find_attributed(word) or word\n", " heads.append(this_head)\n", "\n", " # save all others as heads, check for quantifiers first\n", " elif word_relas == {\"NA\"}:\n", " this_head = find_quantified(word) or find_attributed(word) or word\n", " heads.append(this_head)\n", "\n", " return tuple(sorted(set(heads)))\n", "\n", "\n", "def mother_is_head(word_phrases, previous_heads):\n", "\n", " \"\"\"\n", " Test and validate parallel relationships for independency.\n", " Must gather the mother for each relation and check whether\n", " the mother contains a head word.\n", "\n", " --input--\n", " * list of phrase nodes for a given word (includes subphrases)\n", " * list of previously approved heads\n", "\n", " --output--\n", " boolean\n", " \"\"\"\n", "\n", " # get word's enclosing phrases that are parallel\n", " parallel_phrases = [ph for ph in word_phrases if F.rela.v(ph) in {\"par\", \"Para\"}]\n", " # get the mother for the parallel phrases\n", " parallel_mothers = [E.mother.f(ph)[0] for ph in parallel_phrases]\n", " # get mothers' words, by mother\n", " parallel_mom_words = [set(L.d(mom, \"word\")) for mom in parallel_mothers]\n", " # test for head in each mother\n", " test_mothers = [\n", " bool(phrs_words & set(previous_heads)) for phrs_words in parallel_mom_words\n", " ]\n", "\n", " return all(test_mothers)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How many subphrases with a parallel relation to a validated head consist of more than one word?\n", "\n", "We take the first head element for every noun phrase and check its parallel elements." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "length: 1\n", "\t 3346\n", "length: 2\n", "\t 686\n", "length: 3\n", "\t 86\n", "length: 4\n", "\t 24\n", "length: 9\n", "\t 10\n", "length: 5\n", "\t 4\n", "length: 6\n", "\t 3\n" ] } ], "source": [ "par_word_count = collections.Counter()\n", "par_word_list = collections.defaultdict(list)\n", "\n", "for np in F.typ.s(\"NP\"):\n", "\n", " heads = OLD_get_heads(np)\n", "\n", " if not heads:\n", " continue\n", "\n", " the_head = heads[0]\n", "\n", " if not L.u(the_head, \"subphrase\"):\n", " continue\n", "\n", " head_smallest_sp = sorted(sp for sp in L.u(the_head, \"subphrase\"))[0]\n", "\n", " par_daughter = [d for d in E.mother.t(head_smallest_sp) if F.rela.v(d) == \"par\"]\n", "\n", " for pd in par_daughter:\n", "\n", " word_length = len(L.d(par_daughter[0], \"word\"))\n", "\n", " par_word_count[word_length] += 1\n", " par_word_list[word_length].append((the_head, head_smallest_sp))\n", "\n", "for w_count, count in par_word_count.items():\n", " print(\"length:\", w_count)\n", " print(\"\\t\", count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see some of the larger cases..." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "\n", "\n", "**phrase** *1*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "B.show(par_word_list[6], condenseType=\"phrase\", withNodes=True)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1409887 par 讗止爪职专止芝讜转 诪址讗植讻指謻诇 讜职砖侄讈芝诪侄谉 讜指讬指纸讬执谉變 \n" ] } ], "source": [ "ex_subphrase = par_word_list[6][1][1]\n", "\n", "for daughter in [d for d in E.mother.t(ex_subphrase) if F.rela.v(d) == \"par\"]:\n", "\n", " print(daughter, F.rela.v(daughter), T.text(L.d(daughter, \"word\")))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now some examples of 2 word lengths..." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "\n", "\n", "**phrase** *1*\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n", "\n", "\n", "
\n", "\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "B.show(par_word_list[2][:5], condenseType=\"phrase\", withNodes=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These examples raise an important possibility. If we take the first word labeled \"subs\" (substantive) within the parallel, will that give us the coordinate head?\n", "\n", "Below is an example from Genesis 5:3 which shows a potential pitfall of the method 2 approach, and even of the current approach." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "652841 砖职讈诇止砖执讈证讬诐 讜旨诪职讗址转謾 砖指讈谞指謹讛 \n", "subphrase \ttext \trelation \tmother\n", "砖职讈诇止砖执讈证讬诐 1301044 NA ()\n", "诪职讗址转謾 砖指讈谞指謹讛 1301047 par (1301044,)\n", "诪职讗址转謾 1301045 NA ()\n", "砖指讈谞指謹讛 1301046 rec (2154,)\n" ] } ], "source": [ "example = L.d(T.nodeFromSection((\"Genesis\", 5, 3)), \"phrase\")[3]\n", "\n", "print(example, T.text(L.d(example, \"word\")))\n", "show_subphrases(example)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example above illustrates the shortcoming of even the current method of separating quantifiers and quantifieds, as seen in this result:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'砖职讈诇止砖执讈证讬诐 |砖指讈谞指謹讛 '" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"|\".join(T.text([h]) for h in get_heads(example))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The algorithm retrieves both \"thirty\" and \"year,\" even though the only head in this case is \"year\". This is a shortcoming of the quantifier function, which in this case has not detected a complex quantifier that is formed with a parallel relation.\n", "\n", "The quantifier algorithm should have passed 砖诇砖讬诐 along to another test before validating it as a head. That is, it should look for this case of a complex quantifier. This is actually another good reason to change the parallels finder to approach 2, so that parallels are processed at the head level rather than disconnected from it. In this setup, the algorithm will gather all parallels to the head. If the syntactic head is a quantifier. If it has no quantified noun, then the algorithm will look further at the parallel relationship to see if it is also a quantifier. If it is, then it will look to find that quantifier's substantive and return it instead. This is a complex recursive process that will have to be coded." ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [], "source": [ "# B.show(par_word_list[3][:15]) # uncomment me" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Are there cases where there is multiple coordinate relations with a single subphrase?" ] }, { "cell_type": "code", "execution_count": 160, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_multiple_coor = \"\"\"\n", "\n", "sp1:subphrase\n", "sp2:subphrase rela=par\n", "sp3:subphrase rela=par\n", "\n", "sp2 -mother> sp1\n", "sp3 -mother> sp1\n", "\n", "sp2 # sp3\n", "\"\"\"\n", "\n", "test_multiple_coor = sorted(S.search(test_multiple_coor))\n", "\n", "len(test_multiple_coor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No, it does not happen. Thus, coordinate relations are chained to each other, not multiplied to a single mother." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Conclusions\n", "This inquiry sparked the one above it about the subphrase by subphrase approach. We have decided to table this method for now." ] } ], "metadata": { "jupytext": { "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 2 }