![](\"images/dss-bare.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Help!\n",
"\n",
"Indeed, that is what you need. Click the vertical `Help` tab.\n",
"\n",
"From there, click around a little bit. Don't read closely, just note the kinds of information that is presented to you.\n",
"\n",
"Later on, it will make more sense!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Browsing\n",
"\n",
"First we browse our data. Click the browse button.\n",
"\n",
"![](\"images/dss-browse.png\")
\n",
"\n",
"and then, in the table of *documents* (scrolls), click on a fragment of scroll `1QSb`:\n",
"\n",
"![](\"images/dss-documents.png\")
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you're looking at a fragment of a scroll: the writing in Hebrew characters without vowel signs.\n",
"\n",
"![](\"images/dss-fragment.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now click the *Options* tab and select the `layout-orig-unicode` format to see the same fragment in a layout that indicates the status\n",
"of the pieces of writing.\n",
"\n",
"![](\"images/dss-layout.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can click a triangle to see how a line is broken down:\n",
"\n",
"![](\"images/dss-drill.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Searching\n",
"\n",
"In this corpus there is a lot of attention for the uncertainty of signs and whether they have been corrected, either in antiquity or\n",
"in more modern times.\n",
"\n",
"Also, the corpus is marked up with part-of-speech for each word.\n",
"\n",
"So we can, for example, search for *verbs* that have an uncertain or corrected or removed consonant in them.\n",
"\n",
"```\n",
"word sp=verb\n",
" sign type=cons\n",
" /with/\n",
" .. unc=1|2|3|4\n",
" /or/\n",
" .. cor=1|2|3\n",
" /or/\n",
" .. rem=1|2\n",
" /or/\n",
" .. rec=1\n",
" /-/\n",
"```\n",
"\n",
"![](\"images/dss-search.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In English:\n",
"\n",
"search all `word`s that contain a `sign` with feature `type`\n",
"having value `cons` (consonant) where at least one of the following holds for\n",
"that sign:\n",
"\n",
"* the feature `unc` has value `1` or `2` or `3` or `4`\n",
"* the feature `cor` has value `1` or `2` or `3`\n",
"* the feature `rem` has value `1` or `2`\n",
"* the feature `rec` has value `1`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Words with multiple uncertain signs correspond with multiple results. We can condense the results in such a way that all results for the same word are shown as one result.\n",
"\n",
"Click the options tab, check *condense results*, and check *word* as the container into you want to condense.\n",
"\n",
"![](\"images/dss-search2.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can expand results by clicking the triangle.\n",
"\n",
"You can see the result in context by clicking the browse icon.\n",
"\n",
"You can go back to the result list by clicking the results icon.\n",
"\n",
"![](\"images/dss-back.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Computing\n",
"\n",
"This triggers other questions.\n",
"\n",
"For example: how many verbs are there, if there are already 37344 with uncertain signs?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How is uncertainty distributed over the verbs?\n",
"I.e. how many verbs have how many uncertain/corrected/removed signs?\n",
"\n",
"*This is a typical question where you want to leave the search mode and enter computing mode*.\n",
"\n",
"Let's find out.\n",
"\n",
"Extra information:\n",
"\n",
"* the features `unc`, `cor`, `rem` have values 1, 2, 3, 4 that indicate the kind of uncertainty, correction, removal.\n",
" We just use those values as the seriousness of the uncertainty.\n",
" Essentially, we just sum up all values of these features for each sign.\n",
"* the feature `rec` means, if that the sign is reconstructed. We consider it to be severely uncertain, and add the penalty 10 for\n",
" such signs.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you have followed the installation instructions, you are set.\n",
"Go to the browser window that opened when you gave the command `jupyter notebook` in your terminal.\n",
"\n",
"Then continue reading, and, ... executing.\n",
"\n",
"You can execute a cell by putting your cursor inside it and pressing `Shift Enter`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we load the Text-Fabric module, as follows:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"from tf.app import use"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we load the TF-app for the corpus `dss` and that app loads the corpus data.\n",
"\n",
"We give a name to the result of all that loading: `A`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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"TF-app: ~/github/annotation/app-dss/code"
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