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aloncohen1/TagTogParser.ipynb

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TagTogParser.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "CA4ZzhUWQHmy"
},
"source": [
"### Clone Repo and Import TagTogParser"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_65fg_tucdTN",
"outputId": "75da4ceb-db47-427b-b317-28ae97f5e772"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'tagtog_to_iob'...\n",
"remote: Enumerating objects: 20, done.\u001b[K\n",
"remote: Counting objects: 100% (20/20), done.\u001b[K\n",
"remote: Compressing objects: 100% (15/15), done.\u001b[K\n",
"remote: Total 20 (delta 6), reused 18 (delta 4), pack-reused 0\u001b[K\n",
"Unpacking objects: 100% (20/20), 4.16 KiB | 1.38 MiB/s, done.\n",
"/content/tagtog_to_iob\n",
"Already up to date.\n"
]
}
],
"source": [
"!git clone https://github.com/aloncohen1/tagtog_to_iob.git\n",
"%cd /content/tagtog_to_iob\n",
"!git pull"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "NmLIrobfCoGA"
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('/content')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "26C8cZ4LCyxV",
"outputId": "9abb40e9-e508-4f52-e866-1941fa805060"
},
"outputs": [
],
"source": [
"from tagtog_to_iob import TagTogParser"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Tagtog zipfile to TagTogParser and apply get_iob_tags_df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "ZU2QrG_9dZ-k"
},
"outputs": [],
"source": [
"tagtog_parser = TagTogParser('path_to_tagtog_zip_file.zip')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "53sdRolUdhqq",
"outputId": "c8f035c7-7ba4-458b-853d-711598260ba0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TagTogParser has no tagtog_df. generating it...\n",
"generating tagtog_df...\n",
"converting tags to IOB format...\n",
"500 instances processed\n",
"600 instances processed\n",
"700 instances processed\n",
"800 instances processed\n",
"900 instances processed\n",
"1000 instances processed\n",
"2000 instances processed\n",
"2100 instances processed\n",
"2200 instances processed\n",
"2300 instances processed\n",
"2400 instances processed\n",
"2500 instances processed\n",
"3000 instances processed\n",
"3500 instances processed\n",
"4000 instances processed\n",
"4500 instances processed\n",
"get_iob_tags_df - Done!\n"
]
}
],
"source": [
"# run iob_tags_df\n",
"tagtog_parser.get_iob_tags_df()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"--------------------------------------------------------------------------------<br>\n",
"TagTogParser has parsed the zip data and load into a data frame (**tagtog_df**).<br>\n",
"It contains the following columns:<br>\n",
"a) **file_name** - Document filename (unique id)<br>\n",
"b) **ann** - The original annotation dictionary as it was received by Tagtog <br>\n",
"c) **text** - Plain text, extract from the html files<br>\n",
"--------------------------------------------------------------------------------<br>\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 250
},
"id": "DMGjPCxLEIuR",
"outputId": "30aa95d2-e69e-4739-8371-85219bd2e0e1"
},
"outputs": [
{
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" file_name \\\n",
"0 aOGsUKv3Z2HIQ8lEiIMdUZN5HbMu-social_groups_sam... \n",
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"3 {'annotatable': {'parts': ['s1s1h1', 's1s1v1',... \n",
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"1 Tightening production restrictions in the cont... \n",
"2 Increase in aid for certain productions, such ... \n",
"3 The Mediterranean Corridor: in July 2013, the ... \n",
"4 It mobilizes and supports individuals and poli... "
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"source": [
"tagtog_parser.tagtog_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"--------------------------------------------------------------------------------<br>\n",
"TagTogParser has also processed the \"ann\" and \"text\" of each document and outputs **iob_tags_df**, which contains:<br>\n",
"a) **text_list** - documents tokens split to list <br>\n",
"b) **tags_list** - documents tokens IOB tags split to list <br>\n",
"--------------------------------------------------------------------------------<br>"
]
},
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"outputs": [
{
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"text/plain": [
" text_list \\\n",
"0 [Unemployment, among, migrants, has, more, tha... \n",
"1 [Tightening, production, restrictions, in, the... \n",
"2 [Increase, in, aid, for, certain, productions,... \n",
"3 [The, Mediterranean, Corridor, :, in, July, 20... \n",
"4 [It, mobilizes, and, supports, individuals, an... \n",
"\n",
" tags_list \n",
"0 [O, O, B-social_group, O, O, O, O, O, O, O, O] \n",
"1 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
"2 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
"3 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
"4 [O, O, O, O, B-social_group, O, B-social_group... "
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"metadata": {},
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}
],
"source": [
"tagtog_parser.iob_tags_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analyze the tags distribution"
]
},
{
"cell_type": "code",
"execution_count": 9,
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TagTogParser has no tags_counts. generating it...\n",
"calculating tags counts...\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tagtog_parser.plot_tags_count()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GTKJPtAZ7Hvp",
"outputId": "6a42e21d-bca7-4349-9782-7af9218420dd"
},
"outputs": [
{
"data": {
"text/plain": [
"[('Tightening', 'O'),\n",
" ('production', 'O'),\n",
" ('restrictions', 'O'),\n",
" ('in', 'O'),\n",
" ('the', 'O'),\n",
" ('context', 'O'),\n",
" ('of', 'O'),\n",
" ('EU', 'O'),\n",
" ('enlargement', 'O'),\n",
" ('must', 'O'),\n",
" ('not', 'O'),\n",
" ('be', 'O'),\n",
" ('disproportionately', 'O'),\n",
" ('at', 'O'),\n",
" ('the', 'O'),\n",
" ('expense', 'O'),\n",
" ('of', 'O'),\n",
" ('Dutch', 'B-social_group'),\n",
" ('farmers', 'I-social_group'),\n",
" ('.', 'O')]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(zip(tagtog_parser.iob_tags_df.loc[1,'text_list'],tagtog_parser.iob_tags_df.loc[1,'tags_list']))"
]
}
],
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},
"gpuClass": "standard",
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"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": "3.8.6"
},
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