{ "cells": [ { "cell_type": "markdown", "id": "a522cef7-5dc1-4ffd-9f82-cbb0d6589eff", "metadata": {}, "source": [ "# The particles μέν and δέ (Nestle1904LFT)\n", "\n", "**Work in progress!**" ] }, { "cell_type": "markdown", "id": "401af193-cbf1-47db-a4f4-2d9381c38d42", "metadata": {}, "source": [ "## Table of content \n", "* 1 - Introduction\n", " * 1.1 - Why is this relevant?\n", " * 1.2 - Translating into Text-Fabric queries\n", "* 2 - Load Text-Fabric app and data\n", "* 3 - Performing the queries\n", " * 3.1 - Identifying the use of δέ\n", " * 3.2 - Gathering additional syntactic details regarding the surrounding\n", "* 4 - Discussion\n", "* 5 - Atribution and footnotes\n", "* 6 - Required libraries" ] }, { "cell_type": "markdown", "id": "1aeeb0b9-9e57-4e0c-9db9-6df7935b4396", "metadata": {}, "source": [ "# 1 - Introduction \n", "##### [Back to TOC](#TOC)\n", "\n", "Greek has many particles, such as [μέν](https://stephanus.tlg.uci.edu/lsj/#eid=68439) and [δέ](https://stephanus.tlg.uci.edu/lsj/#eid=24443), that are used to indicate contrast or emphasis. " ] }, { "cell_type": "markdown", "id": "033b35d9-e5e8-42f1-86d2-a8f633a5b248", "metadata": {}, "source": [ "## 1.1 - Why is this relevant? \n", "##### [Back to TOC](#TOC)\n", "\n", "The particle δέ, commonly found in the Greek New Testament, can be used in either an adversative (contrasting) or copulative (confirming) manner, affecting the meaning and interpretation of the surrounding text. The challenge in translating δέ into English arises due to the inherent ambiguity of the particle. English equivalents such as \"but,\" \"and,\" or \"now\" often fail to capture the full range of its meaning.\n", "\n", "In its adversative usage, δέ introduces a contrast or a counterpoint to what has been previously mentioned. It serves to emphasize a distinction or a shift in thought, often presenting an alternative viewpoint or introducing a new topic. This contrasting function of δέ can be theologically significant as it highlights the tensions or conflicts within the text, revealing different perspectives or opposing ideas.\n", "\n", "In its copulative usage, δέ functions as a confirming particle, connecting statements or thoughts in a continuous and cohesive manner. It serves to link ideas together, reinforcing the flow of the discourse. This copulative function of δέ is relevant in conveying theological concepts by maintaining a logical progression in the text, presenting ideas that build upon each other or providing additional supporting information." ] }, { "cell_type": "markdown", "id": "df87da32-a6d2-4c67-99c6-07878382477b", "metadata": {}, "source": [ "## 1.2 - Translating into Text-Fabric queries \n", "##### [Back to TOC](#TOC)\n", "\n", "Identifying the use of δέ is easily done using Text-Fabric. The challenge lies in creating queries that gather relevant information regarding the syntactical surroundings, which could hint at whether δέ is intended adversative or copulative." ] }, { "cell_type": "markdown", "id": "07bd0541-8f54-425d-a5d9-5dd91acac36c", "metadata": {}, "source": [ "# 2 - Load Text-Fabric app and data \n", "##### [Back to TOC](#TOC)" ] }, { "cell_type": "code", "execution_count": 1, "id": "0fb41f21-a71c-44f1-a253-8508dd69779a", "metadata": { "tags": [] }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "id": "6c5469c2-2d60-4b56-9168-deb24692551c", "metadata": {}, "outputs": [], "source": [ "# Loading the Text-Fabric code\n", "# Note: it is assumed Text-Fabric is installed in your environment\n", "from tf.fabric import Fabric\n", "from tf.app import use" ] }, { "cell_type": "code", "execution_count": 3, "id": "6502aec4-b794-4669-94ee-7929041eebea", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/markdown": [ "**Locating corpus resources ...**" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "app: ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/app" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "data: ~/text-fabric-data/github/tonyjurg/Nestle1904LFT/tf/0.6" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " TF: TF API 12.2.2, tonyjurg/Nestle1904LFT/app v3, Search Reference
\n", " Data: tonyjurg - Nestle1904LFT 0.6, Character table, Feature docs
\n", "
Node types\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "
Name# of nodes# slots / node% coverage
book275102.93100
chapter260529.92100
verse794317.35100
sentence801117.20100
wg1054306.85524
word1377791.00100
\n", " Sets: no custom sets
\n", " Features:
\n", "
Nestle 1904 (Low Fat Tree)\n", "
\n", "\n", "
\n", "
\n", "after\n", "
\n", "
str
\n", "\n", " ✅ Characters (eg. punctuations) following the word\n", "\n", "
\n", "\n", "
\n", "
\n", "book\n", "
\n", "
str
\n", "\n", " ✅ Book name (in English language)\n", "\n", "
\n", "\n", "
\n", "
\n", "booknumber\n", "
\n", "
int
\n", "\n", " ✅ NT book number (Matthew=1, Mark=2, ..., Revelation=27)\n", "\n", "
\n", "\n", "
\n", "
\n", "bookshort\n", "
\n", "
str
\n", "\n", " ✅ Book name (abbreviated)\n", "\n", "
\n", "\n", "
\n", "
\n", "case\n", "
\n", "
str
\n", "\n", " ✅ Gramatical case (Nominative, Genitive, Dative, Accusative, Vocative)\n", "\n", "
\n", "\n", "
\n", "
\n", "chapter\n", "
\n", "
int
\n", "\n", " ✅ Chapter number inside book\n", "\n", "
\n", "\n", "
\n", "
\n", "clausetype\n", "
\n", "
str
\n", "\n", " ✅ Clause type details (e.g. Verbless, Minor)\n", "\n", "
\n", "\n", "
\n", "
\n", "containedclause\n", "
\n", "
str
\n", "\n", " 🆗 Contained clause (WG number)\n", "\n", "
\n", "\n", "
\n", "
\n", "degree\n", "
\n", "
str
\n", "\n", " ✅ Degree (e.g. Comparitative, Superlative)\n", "\n", "
\n", "\n", "
\n", "
\n", "gloss\n", "
\n", "
str
\n", "\n", " ✅ English gloss\n", "\n", "
\n", "\n", "
\n", "
\n", "gn\n", "
\n", "
str
\n", "\n", " ✅ Gramatical gender (Masculine, Feminine, Neuter)\n", "\n", "
\n", "\n", "
\n", "
\n", "headverse\n", "
\n", "
str
\n", "\n", " ✅ Start verse number of a sentence\n", "\n", "
\n", "\n", "
\n", "
\n", "junction\n", "
\n", "
str
\n", "\n", " ✅ Junction data related to a wordgroup\n", "\n", "
\n", "\n", "
\n", "
\n", "lemma\n", "
\n", "
str
\n", "\n", " ✅ Lexeme (lemma)\n", "\n", "
\n", "\n", "
\n", "
\n", "lex_dom\n", "
\n", "
str
\n", "\n", " ✅ Lexical domain according to Semantic Dictionary of Biblical Greek, SDBG (not present everywhere?)\n", "\n", "
\n", "\n", "
\n", "
\n", "ln\n", "
\n", "
str
\n", "\n", " ✅ Lauw-Nida lexical classification (not present everywhere?)\n", "\n", "
\n", "\n", "
\n", "
\n", "markafter\n", "
\n", "
str
\n", "\n", " 🆗 Text critical marker after word\n", "\n", "
\n", "\n", "
\n", "
\n", "markbefore\n", "
\n", "
str
\n", "\n", " 🆗 Text critical marker before word\n", "\n", "
\n", "\n", "
\n", "
\n", "markorder\n", "
\n", "
str
\n", "\n", "  Order of punctuation and text critical marker\n", "\n", "
\n", "\n", "
\n", "
\n", "monad\n", "
\n", "
int
\n", "\n", " ✅ Monad (smallest token matching word order in the corpus)\n", "\n", "
\n", "\n", "
\n", "
\n", "mood\n", "
\n", "
str
\n", "\n", " ✅ Gramatical mood of the verb (passive, etc)\n", "\n", "
\n", "\n", "
\n", "
\n", "morph\n", "
\n", "
str
\n", "\n", " ✅ Morphological tag (Sandborg-Petersen morphology)\n", "\n", "
\n", "\n", "
\n", "
\n", "nodeID\n", "
\n", "
str
\n", "\n", " ✅ Node ID (as in the XML source data)\n", "\n", "
\n", "\n", "
\n", "
\n", "normalized\n", "
\n", "
str
\n", "\n", " ✅ Surface word with accents normalized and trailing punctuations removed\n", "\n", "
\n", "\n", "
\n", "
\n", "nu\n", "
\n", "
str
\n", "\n", " ✅ Gramatical number (Singular, Plural)\n", "\n", "
\n", "\n", "
\n", "
\n", "number\n", "
\n", "
str
\n", "\n", " ✅ Gramatical number of the verb (e.g. singular, plural)\n", "\n", "
\n", "\n", "
\n", "
\n", "otype\n", "
\n", "
str
\n", "\n", " \n", "\n", "
\n", "\n", "
\n", "
\n", "person\n", "
\n", "
str
\n", "\n", " ✅ Gramatical person of the verb (first, second, third)\n", "\n", "
\n", "\n", "
\n", "
\n", "punctuation\n", "
\n", "
str
\n", "\n", " ✅ Punctuation after word\n", "\n", "
\n", "\n", "
\n", "
\n", "ref\n", "
\n", "
str
\n", "\n", " ✅ Value of the ref ID (taken from XML sourcedata)\n", "\n", "
\n", "\n", "
\n", "
\n", "reference\n", "
\n", "
str
\n", "\n", " ✅ Reference (to nodeID in XML source data, not yet post-processes)\n", "\n", "
\n", "\n", "
\n", "
\n", "roleclausedistance\n", "
\n", "
str
\n", "\n", " ⚠️ Distance to the wordgroup defining the syntactical role of this word\n", "\n", "
\n", "\n", "
\n", "
\n", "sentence\n", "
\n", "
int
\n", "\n", " ✅ Sentence number (counted per chapter)\n", "\n", "
\n", "\n", "
\n", "
\n", "sp\n", "
\n", "
str
\n", "\n", " ✅ Part of Speech (abbreviated)\n", "\n", "
\n", "\n", "
\n", "
\n", "sp_full\n", "
\n", "
str
\n", "\n", " ✅ Part of Speech (long description)\n", "\n", "
\n", "\n", "
\n", "
\n", "strongs\n", "
\n", "
str
\n", "\n", " ✅ Strongs number\n", "\n", "
\n", "\n", "
\n", "
\n", "subj_ref\n", "
\n", "
str
\n", "\n", " 🆗 Subject reference (to nodeID in XML source data, not yet post-processes)\n", "\n", "
\n", "\n", "
\n", "
\n", "tense\n", "
\n", "
str
\n", "\n", " ✅ Gramatical tense of the verb (e.g. Present, Aorist)\n", "\n", "
\n", "\n", "
\n", "
\n", "type\n", "
\n", "
str
\n", "\n", " ✅ Gramatical type of noun or pronoun (e.g. Common, Personal)\n", "\n", "
\n", "\n", "
\n", "
\n", "unicode\n", "
\n", "
str
\n", "\n", " ✅ Word as it apears in the text in Unicode (incl. punctuations)\n", "\n", "
\n", "\n", "
\n", "
\n", "verse\n", "
\n", "
int
\n", "\n", " ✅ Verse number inside chapter\n", "\n", "
\n", "\n", "
\n", "
\n", "voice\n", "
\n", "
str
\n", "\n", " ✅ Gramatical voice of the verb (e.g. active,passive)\n", "\n", "
\n", "\n", "
\n", "
\n", "wgclass\n", "
\n", "
str
\n", "\n", " ✅ Class of the wordgroup (e.g. cl, np, vp)\n", "\n", "
\n", "\n", "
\n", "
\n", "wglevel\n", "
\n", "
int
\n", "\n", " 🆗 Number of the parent wordgroups for a wordgroup\n", "\n", "
\n", "\n", "
\n", "
\n", "wgnum\n", "
\n", "
int
\n", "\n", " ✅ Wordgroup number (counted per book)\n", "\n", "
\n", "\n", "
\n", "
\n", "wgrole\n", "
\n", "
str
\n", "\n", " ✅ Syntactical role of the wordgroup (abbreviated)\n", "\n", "
\n", "\n", "
\n", "
\n", "wgrolelong\n", "
\n", "
str
\n", "\n", " ✅ Syntactical role of the wordgroup (full)\n", "\n", "
\n", "\n", "
\n", "
\n", "wgrule\n", "
\n", "
str
\n", "\n", " ✅ Wordgroup rule information (e.g. Np-Appos, ClCl2, PrepNp)\n", "\n", "
\n", "\n", "
\n", "
\n", "wgtype\n", "
\n", "
str
\n", "\n", " ✅ Wordgroup type details (e.g. group, apposition)\n", "\n", "
\n", "\n", "
\n", "
\n", "word\n", "
\n", "
str
\n", "\n", " ✅ Word as it appears in the text (excl. punctuations)\n", "\n", "
\n", "\n", "
\n", "
\n", "wordlevel\n", "
\n", "
str
\n", "\n", " 🆗 Number of the parent wordgroups for a word\n", "\n", "
\n", "\n", "
\n", "
\n", "wordrole\n", "
\n", "
str
\n", "\n", " ✅ Syntactical role of the word (abbreviated)\n", "\n", "
\n", "\n", "
\n", "
\n", "wordrolelong\n", "
\n", "
str
\n", "\n", " ✅ Syntactical role of the word (full)\n", "\n", "
\n", "\n", "
\n", "
\n", "wordtranslit\n", "
\n", "
str
\n", "\n", " 🆗 Transliteration of the text (in latin letters, excl. punctuations)\n", "\n", "
\n", "\n", "
\n", "
\n", "wordunacc\n", "
\n", "
str
\n", "\n", " ✅ Word without accents (excl. punctuations)\n", "\n", "
\n", "\n", "
\n", "
\n", "oslots\n", "
\n", "
none
\n", "\n", " \n", "\n", "
\n", "\n", "
\n", "
\n", "\n", " Settings:
specified
  1. apiVersion: 3
  2. appName: tonyjurg/Nestle1904LFT
  3. appPath:C:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904LFT/app
  4. commit: e68bd68c7c4c862c1464d995d51e27db7691254f
  5. css: ''
  6. dataDisplay:
    • excludedFeatures:
      • orig_order
      • verse
      • book
      • chapter
    • noneValues:
      • none
      • unknown
      • no value
      • NA
      • ''
    • showVerseInTuple: 0
    • textFormat: text-orig-full
  7. docs:
    • docBase: https://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/
    • docPage: about
    • docRoot: https://github.com/tonyjurg/Nestle1904LFT
    • featureBase:https://github.com/tonyjurg/Nestle1904LFT/blob/main/docs/features/<feature>.md
  8. interfaceDefaults: {fmt: layout-orig-full}
  9. isCompatible: True
  10. local: local
  11. localDir:C:/Users/tonyj/text-fabric-data/github/tonyjurg/Nestle1904LFT/_temp
  12. provenanceSpec:
    • corpus: Nestle 1904 (Low Fat Tree)
    • doi: 10.5281/zenodo.10182594
    • org: tonyjurg
    • relative: /tf
    • repo: Nestle1904LFT
    • repro: Nestle1904LFT
    • version: 0.6
    • webBase: https://learner.bible/text/show_text/nestle1904/
    • webHint: Show this on the Bible Online Learner website
    • webLang: en
    • webUrl:https://learner.bible/text/show_text/nestle1904/<1>/<2>/<3>
    • webUrlLex: {webBase}/word?version={version}&id=<lid>
  13. release: v0.6
  14. typeDisplay:
    • book:
      • condense: True
      • hidden: True
      • label: {book}
      • style: ''
    • chapter:
      • condense: True
      • hidden: True
      • label: {chapter}
      • style: ''
    • sentence:
      • hidden: 0
      • label: #{sentence} (start: {book} {chapter}:{headverse})
      • style: ''
    • verse:
      • condense: True
      • excludedFeatures: chapter verse
      • label: {book} {chapter}:{verse}
      • style: ''
    • wg:
      • hidden: 0
      • label:#{wgnum}: {wgtype} {wgclass} {clausetype} {wgrole} {wgrule} {junction}
      • style: ''
    • word:
      • base: True
      • features: lemma
      • featuresBare: gloss
      • surpress: chapter verse
  15. writing: grc
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
TF API: names N F E L T S C TF Fs Fall Es Eall Cs Call directly usable
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# load the N1904 app and data\n", "N1904 = use (\"tonyjurg/Nestle1904LFT\", version=\"0.6\", hoist=globals())" ] }, { "cell_type": "code", "execution_count": 4, "id": "918e0518-5f14-4785-9e11-58e5657ccc96", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The following will push the Text-Fabric stylesheet to this notebook (to facilitate proper display with notebook viewer)\n", "N1904.dh(N1904.getCss())" ] }, { "cell_type": "code", "execution_count": 5, "id": "7f53ce86-9b87-40b1-a950-4d9abd2b5373", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Set default view in a way to limit noise as much as possible.\n", "N1904.displaySetup(condensed=True, multiFeatures=False,queryFeatures=False)" ] }, { "cell_type": "markdown", "id": "994fea19-a622-44ce-9b7a-35c0a124a384", "metadata": {}, "source": [ "# 3 - Performing the queries \n", "##### [Back to TOC](#TOC)" ] }, { "cell_type": "markdown", "id": "7bd746f0-4a5d-4611-818d-ff2418281d84", "metadata": { "tags": [] }, "source": [ "## 3.1 - Identifying the use of δέ \n", "##### [Back to TOC](#TOC)\n", "\n", "This can be done using a straight forward query. The node numbers of sentence,clause and phrase containing the δέ will also be gathered allowing easier further processing." ] }, { "cell_type": "code", "execution_count": 6, "id": "9c1684e7-7175-4e6b-9957-b3bdebfe066d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.09s 2787 results\n" ] } ], "source": [ "# Define the query template\n", "DeQuery= '''\n", "word lemma=δέ\n", "'''\n", "\n", "# The following will create a list containing ordered tuples consisting of node numbers of the items as they appear in the query\n", "DeResult = N1904.search(DeQuery)" ] }, { "cell_type": "markdown", "id": "88d1c3bd-0a94-4b39-9e6f-c913f2234eb7", "metadata": {}, "source": [ "## 3.2 - Position of δέ in a clause\n", "##### [Back to TOC](#TOC)\n", "\n", "TBD." ] }, { "cell_type": "code", "execution_count": 10, "id": "35b7bb60-569d-48c9-90d0-d04131c4112a", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Position frequency list: {2: 2687, 3: 75, 4: 14, 5: 2, 1: 1, 12: 1, 7: 3, 9: 2, 6: 1, 11: 1}\n" ] } ], "source": [ "import unicodedata\n", "import string\n", "from unidecode import unidecode\n", "\n", "def remove_punctuation(input_string):\n", " # Create a string of all punctuation characters\n", " punctuation_chars = \".,*\"\n", " \n", " # Use str.translate to replace punctuation characters with empty string\n", " result_string = input_string.translate(str.maketrans(\"\", \"\", punctuation_chars))\n", " \n", " return result_string\n", "\n", "def fix_abbreviated(input_string):\n", " fixed_string = input_string.replace(\"d'\", \"de\")\n", " return fixed_string\n", "\n", "\n", "# small function to find position of a word\n", "def find_word_position(sentence, target_word):\n", " words = sentence.split()\n", " try:\n", " position = words.index(target_word) + 1 \n", " # Adding 1 to make it more 'natural' (i.e. 1-based index)\n", " return position\n", " except ValueError:\n", " # following print reveals any occurence of 'de' which is not accounted for\n", " print ('NOT:',sentence)\n", " return -1 # Word not found in the sentence\n", " \n", "target_word = unidecode('δέ')\n", "position_frequency = {}\n", " \n", "# DeResult is a list of tuples each consisting of two integers, we need the second one _,\n", "for word in DeResult:\n", " # get first item from tuple of integers \n", " parent_wg=L.u(word[0])[0]\n", " # decoded text of the parent wordgroup with punctuations removed and abreviations 'repaired'\n", " parent_wg_text=fix_abbreviated(remove_punctuation(unidecode(T.text(parent_wg))))\n", " position = find_word_position(parent_wg_text, target_word)\n", " # Check if the position is found\n", " if position != -1:\n", " # Update the frequency dictionary\n", " position_frequency[position] = position_frequency.get(position, 0) + 1\n", "\n", "# Print the frequency list\n", "print(\"Position frequency list:\", position_frequency)" ] }, { "cell_type": "markdown", "id": "7633a33d-524c-43a4-b541-7f884f9fa8f3", "metadata": {}, "source": [ "# 4 - Discussion \n", "##### [Back to TOC](#TOC)\n", "\n", "TBA" ] }, { "cell_type": "markdown", "id": "9bf000a7-408c-4502-ae58-289a851aa831", "metadata": {}, "source": [ "# 5 - Attribution and footnotes\n", "##### [Back to TOC](#TOC)\n", "\n", "NA" ] }, { "cell_type": "markdown", "id": "284a1216-ecbd-48eb-9435-2cb54eaf132b", "metadata": { "tags": [] }, "source": [ "# 6 - Required libraries \n", "##### [Back to TOC](#TOC)\n", "\n", "The scripts in this notebook require (beside `text-fabric`) the following Python libraries to be installed in the environment:\n", "\n", " {none}\n", "\n", "You can install any missing library from within Jupyter Notebook using either`pip` or `pip3`." ] }, { "cell_type": "code", "execution_count": null, "id": "206c5c72-6362-4cad-91ce-6a78224ebb1e", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }