FE_Supporting_Links_NER_edsu.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SpeHibOvHNB8"
   },
   "source": [
    "# Basic NER of Fatal Encounter Supporting Links\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook analyzes the supporting links in the [Fatal Encounters dataset](https://fatalencounters.org/tools-for-journalists/) and attempts to extract links from the text of newspaper articles that are linked to each case.\n",
    "\n",
    "First lets install some of the Python dependencies that will be needed in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 41231,
     "status": "ok",
     "timestamp": 1690567345610,
     "user": {
      "displayName": "Edward Hugh Summers",
      "userId": "00384552605219110914"
     },
     "user_tz": 240
    },
    "id": "T4jf-jWUeiZg",
    "outputId": "e5cb7015-2080-4d05-978c-3ef4da70bad1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install --quiet transformers newspaper3k pyvis spacy pandas ipywidgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When rerunning portions of the notebook it's helpful to have all the imports in one cell. These will make more sense later as they are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import logging\n",
    "import spacy\n",
    "import torch\n",
    "\n",
    "from newspaper import Article, ArticleException\n",
    "from pathlib import Path\n",
    "from tqdm.notebook import tqdm\n",
    "from glob import glob\n",
    "from pathlib import Path\n",
    "from tqdm.notebook import tqdm\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Article Text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DZiU4mdrtQ0o"
   },
   "source": [
    "Create a function that uses the newspaper module to parse a newspaper article from the web. Log any errors that are encountered for analysis later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "executionInfo": {
     "elapsed": 737,
     "status": "ok",
     "timestamp": 1690570198701,
     "user": {
      "displayName": "Edward Hugh Summers",
      "userId": "00384552605219110914"
     },
     "user_tz": 240
    },
    "id": "NEyKQChIeKnZ",
    "outputId": "a076af39-4feb-465a-f099-7418e6965bc3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'City to Appeal Judgment in Police-Custody Death'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logger = logging.basicConfig(filename=\"fe-links.log\", level=logging.INFO)\n",
    "\n",
    "def get_article(url):\n",
    "    try:\n",
    "        logging.info('fetching url: %s', url)\n",
    "        article = Article(url)\n",
    "        article.download()\n",
    "        article.parse()\n",
    "        return article\n",
    "    except ArticleException as e:\n",
    "        logging.error(\"caught article error: {}\".format(e))\n",
    "        return None\n",
    "\n",
    "article = get_article('https://www.latimes.com/archives/la-xpm-2002-may-10-me-award10-story.html')\n",
    "article.title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5gif4bm5tyPk"
   },
   "source": [
    "## Download All Articles\n",
    "\n",
    "Use the Fatal Encounters Google Sheet to download all the text for each case. It can take between 1 and 2 seconds to download each article , and there are over 30,000 articles, so this will take about 8 hours. It's helpful to do this step separate from the entity extraction since we know we want to try multiple methods for entity extraction and we wouldn't want to have to rerun the text fetching each time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 8404,
     "status": "ok",
     "timestamp": 1690568686053,
     "user": {
      "displayName": "Edward Hugh Summers",
      "userId": "00384552605219110914"
     },
     "user_tz": 240
    },
    "id": "7TC9bxQf0H2A"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unique ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>Race with imputations</th>\n",
       "      <th>Imputation probability</th>\n",
       "      <th>URL of image (PLS NO HOTLINKS)</th>\n",
       "      <th>Date of injury resulting in death (month/day/year)</th>\n",
       "      <th>Location of injury (address)</th>\n",
       "      <th>...</th>\n",
       "      <th>URL Temp</th>\n",
       "      <th>Brief description</th>\n",
       "      <th>Dispositions/Exclusions INTERNAL USE, NOT FOR ANALYSIS</th>\n",
       "      <th>Intended use of force (Developing)</th>\n",
       "      <th>Supporting document link</th>\n",
       "      <th>Foreknowledge of mental illness? INTERNAL USE, NOT FOR ANALYSIS</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "      <th>Unnamed: 33</th>\n",
       "      <th>Unique ID formula</th>\n",
       "      <th>Unique identifier (redundant)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31495.0</td>\n",
       "      <td>Ashley McClendon</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>African-American/Black</td>\n",
       "      <td>African-American/Black</td>\n",
       "      <td>Not imputed</td>\n",
       "      <td>https://fatalencounters.org/wp-content/uploads...</td>\n",
       "      <td>12/31/2021</td>\n",
       "      <td>South Pearl Street and Tory Road</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ashley McClendon's boyfriend, 33-year-old Marc...</td>\n",
       "      <td>Criminal</td>\n",
       "      <td>Pursuit</td>\n",
       "      <td>https://www.wsoctv.com/news/1-person-dead-afte...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31496.0</td>\n",
       "      <td>Name withheld by police</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>Race unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/31/2021</td>\n",
       "      <td>1500 21st Street</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Police responded to a man causing a disturbanc...</td>\n",
       "      <td>Pending investigation</td>\n",
       "      <td>Deadly force</td>\n",
       "      <td>https://www.wtok.com/2022/01/01/officer-involv...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31497.0</td>\n",
       "      <td>Name withheld by police</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>Race unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/31/2021</td>\n",
       "      <td>1500 21st Street</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Police responded to a man causing a disturbanc...</td>\n",
       "      <td>Pending investigation</td>\n",
       "      <td>Deadly force</td>\n",
       "      <td>https://www.wtok.com/2022/01/01/officer-involv...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31491.0</td>\n",
       "      <td>Johnny C. Martin Jr.</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Race unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/30/2021</td>\n",
       "      <td>Martinez Lane</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnny C. Martin, Jr. arrived at a gas station...</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>Suicide</td>\n",
       "      <td>https://gbi.georgia.gov/press-releases/2021-12...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31492.0</td>\n",
       "      <td>Dennis McHugh</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>European-American/White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/30/2021</td>\n",
       "      <td>435 E 4th Street</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Deputies responded to a domestic violence call...</td>\n",
       "      <td>Pending investigation</td>\n",
       "      <td>Deadly force</td>\n",
       "      <td>https://kesq.com/news/2021/12/31/officer-invol...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31492.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unique ID                     Name   Age  Gender                     Race  \\\n",
       "0    31495.0         Ashley McClendon  28.0  Female   African-American/Black   \n",
       "1    31496.0  Name withheld by police   NaN  Female         Race unspecified   \n",
       "2    31497.0  Name withheld by police   NaN    Male         Race unspecified   \n",
       "3    31491.0     Johnny C. Martin Jr.  36.0    Male         Race unspecified   \n",
       "4    31492.0            Dennis McHugh  44.0    Male  European-American/White   \n",
       "\n",
       "    Race with imputations Imputation probability  \\\n",
       "0  African-American/Black            Not imputed   \n",
       "1                     NaN                    NaN   \n",
       "2                     NaN                    NaN   \n",
       "3                     NaN                    NaN   \n",
       "4                     NaN                    NaN   \n",
       "\n",
       "                      URL of image (PLS NO HOTLINKS)  \\\n",
       "0  https://fatalencounters.org/wp-content/uploads...   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "   Date of injury resulting in death (month/day/year)  \\\n",
       "0                                         12/31/2021    \n",
       "1                                         12/31/2021    \n",
       "2                                         12/31/2021    \n",
       "3                                         12/30/2021    \n",
       "4                                         12/30/2021    \n",
       "\n",
       "       Location of injury (address)  ... URL Temp  \\\n",
       "0  South Pearl Street and Tory Road  ...      NaN   \n",
       "1                  1500 21st Street  ...      NaN   \n",
       "2                  1500 21st Street  ...      NaN   \n",
       "3                     Martinez Lane  ...      NaN   \n",
       "4                  435 E 4th Street  ...      NaN   \n",
       "\n",
       "                                   Brief description  \\\n",
       "0  Ashley McClendon's boyfriend, 33-year-old Marc...   \n",
       "1  Police responded to a man causing a disturbanc...   \n",
       "2  Police responded to a man causing a disturbanc...   \n",
       "3  Johnny C. Martin, Jr. arrived at a gas station...   \n",
       "4  Deputies responded to a domestic violence call...   \n",
       "\n",
       "   Dispositions/Exclusions INTERNAL USE, NOT FOR ANALYSIS  \\\n",
       "0                                           Criminal        \n",
       "1                              Pending investigation        \n",
       "2                              Pending investigation        \n",
       "3                                            Suicide        \n",
       "4                              Pending investigation        \n",
       "\n",
       "  Intended use of force (Developing)  \\\n",
       "0                            Pursuit   \n",
       "1                       Deadly force   \n",
       "2                       Deadly force   \n",
       "3                            Suicide   \n",
       "4                       Deadly force   \n",
       "\n",
       "                            Supporting document link  \\\n",
       "0  https://www.wsoctv.com/news/1-person-dead-afte...   \n",
       "1  https://www.wtok.com/2022/01/01/officer-involv...   \n",
       "2  https://www.wtok.com/2022/01/01/officer-involv...   \n",
       "3  https://gbi.georgia.gov/press-releases/2021-12...   \n",
       "4  https://kesq.com/news/2021/12/31/officer-invol...   \n",
       "\n",
       "   Foreknowledge of mental illness? INTERNAL USE, NOT FOR ANALYSIS  \\\n",
       "0                                                 No                 \n",
       "1                                                 No                 \n",
       "2                                                 No                 \n",
       "3                                                 No                 \n",
       "4                                                 No                 \n",
       "\n",
       "   Unnamed: 32 Unnamed: 33 Unique ID formula  Unique identifier (redundant)  \n",
       "0          NaN         NaN               NaN                        31495.0  \n",
       "1          NaN         NaN               NaN                        31496.0  \n",
       "2          NaN         NaN               NaN                        31497.0  \n",
       "3          NaN         NaN               NaN                        31491.0  \n",
       "4          NaN         NaN               NaN                        31492.0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet_id = \"1dKmaV_JiWcG8XBoRgP8b4e9Eopkpgt7FL7nyspvzAsE\"\n",
    "sheet_name = \"sample_1\"\n",
    "url = f\"https://docs.google.com/spreadsheets/d/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}\"\n",
    "df_fe = pd.read_csv(url)\n",
    "df_fe.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the text of each article and write to the filesystem using the ID. If the file is already present there's no need to redownload. This lets you stop the collecting and restart later if you are running on your laptop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "a008bb567a5f4b5699710e846cec5e28",
      "a52472ebdfda4842bcda5ca10c8c9f60",
      "85c362acab4c446a8a1cee9f8fa40cba",
      "6b715d6cf4b3458483b569c5cdc709b6",
      "62beb3246e4c4a28a77dbb25aa3c33eb",
      "7ecf00a9f581461895a4a32e1fb47dcb",
      "cc7035df312441cbb95888bdb954e647",
      "29d18f6a41ec4af2a2cedbc24e80db00",
      "64bea5aaef8d4a80aebc87a6ec7f9ff8",
      "771a5d8cbe4e47ffb4ab32eeac423a04",
      "23b0d3f52ac44ac09c56b6082b2aad56"
     ]
    },
    "id": "ZE-Knla_euBP",
    "outputId": "06947ba3-1491-4de3-acbf-5dea38deeb7c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d257e9c96c1e488a915e92f54f3370db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/31496 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ignore any spreadsheet rows without an ID or URL\n",
    "df_fe = df_fe.dropna(subset=['Unique ID', 'Supporting document link'])\n",
    "\n",
    "for i, row in tqdm(list(df_fe.iterrows())):\n",
    "    fe_id = int(row['Unique ID'])\n",
    "    \n",
    "    text_file = Path(\"data\") / f\"{fe_id}.txt\"\n",
    "    if text_file.is_file():\n",
    "        continue\n",
    "\n",
    "    url = row['Supporting document link']\n",
    "    article = get_article(url)\n",
    "    if article:\n",
    "        text_file.open(\"w\").write(article.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OFJU7eqTtjhZ"
   },
   "source": [
    "Create a function that will get the entities from some text using spacy's [en_core_web_sm](https://spacy.io/models/en/#en_core_web_sm) model, which we may need to download:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! spacy download en_core_web_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1327,
     "status": "ok",
     "timestamp": 1690569095005,
     "user": {
      "displayName": "Edward Hugh Summers",
      "userId": "00384552605219110914"
     },
     "user_tz": 240
    },
    "id": "N6zZj7PhtOhM",
    "outputId": "e49155cd-52d8-47c1-cf19-93033f44587f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'text': '$1-million', 'label': 'MONEY'},\n",
       " {'text': 'Army', 'label': 'ORG'},\n",
       " {'text': 'Los Angeles', 'label': 'GPE'},\n",
       " {'text': 'Thursday', 'label': 'DATE'},\n",
       " {'text': 'Anthony Eady', 'label': 'PERSON'},\n",
       " {'text': '52', 'label': 'DATE'},\n",
       " {'text': 'three days', 'label': 'DATE'},\n",
       " {'text': 'Aug. 26, 2000', 'label': 'DATE'},\n",
       " {'text': 'Amar Eady', 'label': 'PERSON'},\n",
       " {'text': '28', 'label': 'DATE'},\n",
       " {'text': '669,100', 'label': 'MONEY'},\n",
       " {'text': 'Rashidah Thomas-Eady', 'label': 'PERSON'},\n",
       " {'text': '24', 'label': 'DATE'},\n",
       " {'text': '350,000', 'label': 'MONEY'},\n",
       " {'text': 'Wednesday', 'label': 'DATE'},\n",
       " {'text': 'Compton', 'label': 'ORG'},\n",
       " {'text': 'Los Angeles County Superior Court', 'label': 'GPE'},\n",
       " {'text': 'Marlene Kristovich', 'label': 'PERSON'},\n",
       " {'text': 'Jeffrey Wilson', 'label': 'PERSON'},\n",
       " {'text': 'Randy McMurray', 'label': 'PERSON'},\n",
       " {'text': 'City Atty', 'label': 'PERSON'},\n",
       " {'text': 'Richard M. Arias', 'label': 'PERSON'},\n",
       " {'text': 'Eady', 'label': 'PERSON'},\n",
       " {'text': '0.448', 'label': 'CARDINAL'},\n",
       " {'text': 'more than five', 'label': 'CARDINAL'},\n",
       " {'text': 'Arias', 'label': 'PERSON'},\n",
       " {'text': 'Wilson', 'label': 'ORG'},\n",
       " {'text': 'LAPD', 'label': 'ORG'},\n",
       " {'text': 'Arias', 'label': 'PERSON'},\n",
       " {'text': 'Thursday', 'label': 'DATE'},\n",
       " {'text': 'two', 'label': 'CARDINAL'},\n",
       " {'text': 'African American', 'label': 'NORP'},\n",
       " {'text': 'Wilson', 'label': 'ORG'},\n",
       " {'text': 'three', 'label': 'CARDINAL'},\n",
       " {'text': 'Latinos', 'label': 'ORG'},\n",
       " {'text': 'four', 'label': 'CARDINAL'},\n",
       " {'text': 'five', 'label': 'CARDINAL'},\n",
       " {'text': 'Arias', 'label': 'PERSON'},\n",
       " {'text': 'Louis Pena', 'label': 'PERSON'},\n",
       " {'text': 'McMurray', 'label': 'ORG'},\n",
       " {'text': 'Arias', 'label': 'PERSON'},\n",
       " {'text': 'Pena', 'label': 'PERSON'},\n",
       " {'text': 'McMurray', 'label': 'ORG'},\n",
       " {'text': '100,000', 'label': 'MONEY'},\n",
       " {'text': 'McMurray', 'label': 'PERSON'},\n",
       " {'text': 'at least $750,000', 'label': 'MONEY'}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "def get_entities(text):\n",
    "    doc = nlp(text)\n",
    "    for ent in doc.ents:\n",
    "        yield {\"text\": ent.text, \"label\": ent.label_}\n",
    "\n",
    "article = get_article('https://www.latimes.com/archives/la-xpm-2002-may-10-me-award10-story.html')\n",
    "list(get_entities(article.text))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PPw3Ui0jxzaV"
   },
   "source": [
    "Fetch all the entities from each text file and write them as a CSV file along side it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "a008bb567a5f4b5699710e846cec5e28",
      "a52472ebdfda4842bcda5ca10c8c9f60",
      "85c362acab4c446a8a1cee9f8fa40cba",
      "6b715d6cf4b3458483b569c5cdc709b6",
      "62beb3246e4c4a28a77dbb25aa3c33eb",
      "7ecf00a9f581461895a4a32e1fb47dcb",
      "cc7035df312441cbb95888bdb954e647",
      "29d18f6a41ec4af2a2cedbc24e80db00",
      "64bea5aaef8d4a80aebc87a6ec7f9ff8",
      "771a5d8cbe4e47ffb4ab32eeac423a04",
      "23b0d3f52ac44ac09c56b6082b2aad56"
     ]
    },
    "id": "ZE-Knla_euBP",
    "outputId": "06947ba3-1491-4de3-acbf-5dea38deeb7c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "276308124fa6494aac1dd081ef6356a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/24386 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for text_file in tqdm(glob('data/*.txt')):\n",
    "    text_file = Path(text_file)\n",
    "    fe_id = text_file.stem\n",
    "    csv_filename = Path(\"data\") / f\"{fe_id}-spacy.csv\"\n",
    "\n",
    "    # don't regenerate if we have it already\n",
    "    if csv_filename.is_file():\n",
    "        continue\n",
    "\n",
    "    text = text_file.open().read()\n",
    "    df = pd.DataFrame(get_entities(text))\n",
    "    df['fe_id'] = fe_id\n",
    "    \n",
    "    if len(df) > 0:\n",
    "        df.to_csv(csv_filename, index=False)\n",
    "        logging.info('wrote %s', csv_filename)\n",
    "    else:\n",
    "        logging.info('no spacy entities for %s', text_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load in all the CSVs into one DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>fe_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Homewood</td>\n",
       "      <td>GPE</td>\n",
       "      <td>30113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>DATE</td>\n",
       "      <td>30113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42-year-old</td>\n",
       "      <td>DATE</td>\n",
       "      <td>30113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Homewood</td>\n",
       "      <td>ORG</td>\n",
       "      <td>30113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>CARDINAL</td>\n",
       "      <td>30113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Harbison</td>\n",
       "      <td>PERSON</td>\n",
       "      <td>18411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Park Ridge Hospital</td>\n",
       "      <td>FAC</td>\n",
       "      <td>18411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Harbison</td>\n",
       "      <td>PERSON</td>\n",
       "      <td>18411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>The North Carolina State Highway Patrol</td>\n",
       "      <td>ORG</td>\n",
       "      <td>18411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>the North Carolina State Bureau of Investigation</td>\n",
       "      <td>ORG</td>\n",
       "      <td>18411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1107761 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text     label  fe_id\n",
       "0                                           Homewood       GPE  30113\n",
       "1                                            Tuesday      DATE  30113\n",
       "2                                        42-year-old      DATE  30113\n",
       "3                                           Homewood       ORG  30113\n",
       "4                                                two  CARDINAL  30113\n",
       "..                                               ...       ...    ...\n",
       "36                                          Harbison    PERSON  18411\n",
       "37                               Park Ridge Hospital       FAC  18411\n",
       "38                                          Harbison    PERSON  18411\n",
       "39           The North Carolina State Highway Patrol       ORG  18411\n",
       "40  the North Carolina State Bureau of Investigation       ORG  18411\n",
       "\n",
       "[1107761 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ents_spacy = pd.concat([\n",
    "    pd.read_csv(csv_file) for csv_file in glob('data/*-spacy.csv')\n",
    "])\n",
    "ents_spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "PERSON         280606\n",
       "DATE           188609\n",
       "ORG            186063\n",
       "GPE            154273\n",
       "CARDINAL       142616\n",
       "TIME            53631\n",
       "FAC             31123\n",
       "ORDINAL         15007\n",
       "LOC             13805\n",
       "PRODUCT          9131\n",
       "NORP             8755\n",
       "WORK_OF_ART      7563\n",
       "QUANTITY         7187\n",
       "MONEY            4295\n",
       "EVENT            1852\n",
       "LAW              1628\n",
       "PERCENT          1213\n",
       "LANGUAGE          404\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ents_spacy.value_counts('label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the top 50 mentioned organizations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text\n",
       "MIAMI                                                                                             4051\n",
       "SWAT                                                                                              2369\n",
       "Taser                                                                                             1676\n",
       "State                                                                                             1222\n",
       "FBI                                                                                               1008\n",
       "Court                                                                                              971\n",
       "Ford                                                                                               883\n",
       "CHP                                                                                                787\n",
       "HIALEAH                                                                                            784\n",
       "Honda                                                                                              739\n",
       "Toyota                                                                                             732\n",
       "Smith                                                                                              594\n",
       "Jeep                                                                                               590\n",
       "GBI                                                                                                551\n",
       "LAKELAND                                                                                           547\n",
       "TALLAHASSEE                                                                                        540\n",
       "GAINESVILLE                                                                                        540\n",
       "Office                                                                                             508\n",
       "Thompson                                                                                           506\n",
       "Wilson                                                                                             491\n",
       "CPR                                                                                                467\n",
       "BMW                                                                                                467\n",
       "Nissan                                                                                             441\n",
       "Marshall Islands American Samoa Federated States                                                   401\n",
       "Virgin Islands Armed Forces Americas Armed Forces Pacific Armed Forces Europe Northern Mariana     401\n",
       "Canada Yukon Territory                                                                             401\n",
       "AP                                                                                                 400\n",
       "Moore                                                                                              388\n",
       "Defendants                                                                                         386\n",
       "Chevrolet                                                                                          376\n",
       "EMS                                                                                                372\n",
       "Times                                                                                              359\n",
       "NYPD                                                                                               343\n",
       "Dodge                                                                                              336\n",
       "TBI                                                                                                335\n",
       "Lexus                                                                                              314\n",
       "Bell                                                                                               302\n",
       "PENSACOLA                                                                                          302\n",
       "MELBOURNE                                                                                          301\n",
       "Campbell                                                                                           292\n",
       "McDonald                                                                                           286\n",
       "Cadillac                                                                                           284\n",
       "HPD                                                                                                282\n",
       "Martin                                                                                             276\n",
       "Plaintiff                                                                                          276\n",
       "the Sheriff's Office                                                                               271\n",
       "Walmart                                                                                            268\n",
       "LAPD                                                                                               266\n",
       "Turner                                                                                             261\n",
       "Tasers                                                                                             256\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 50)\n",
    "\n",
    "ents_spacy[ents_spacy.label == 'ORG'].value_counts('text')[0:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about people?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text\n",
       "Sgt                    1757\n",
       "Johnson                1713\n",
       "Williams               1707\n",
       "Brown                  1411\n",
       "Jones                  1147\n",
       "marijuana               999\n",
       "Davis                   984\n",
       "Thomas                  901\n",
       "Taylor                  860\n",
       "Garcia                  772\n",
       "Harris                  743\n",
       "Jackson                 743\n",
       "Smith                   711\n",
       "Anderson                696\n",
       "Martinez                688\n",
       "Lewis                   636\n",
       "Miller                  614\n",
       "Robinson                569\n",
       "Martin                  539\n",
       "Clark                   507\n",
       "Evans                   466\n",
       "Lee                     461\n",
       "Gonzalez                448\n",
       "Baker                   429\n",
       "Zip Code                401\n",
       "Canada Newfoundland     401\n",
       "Perez                   396\n",
       "Carter                  356\n",
       "Twitter                 355\n",
       "Nelson                  351\n",
       "Wright                  350\n",
       "Corey                   347\n",
       "Gilbert                 344\n",
       "Christopher             341\n",
       "Cpl                     339\n",
       "Graham                  332\n",
       "Stewart                 320\n",
       "Latino                  304\n",
       "Allen                   302\n",
       "Scott                   299\n",
       "Walgreens               293\n",
       "Coleman                 292\n",
       "Ramirez                 287\n",
       "Roberts                 275\n",
       "Ramsey                  274\n",
       "Howard                  271\n",
       "Mitchell                270\n",
       "Patterson               265\n",
       "David                   262\n",
       "Powell                  261\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ents_spacy[ents_spacy.label == 'PERSON'].value_counts('text')[0:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe we should remove single names?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text\n",
       "Canada Newfoundland     401\n",
       "Zip Code                401\n",
       "BONITA SPRINGS          240\n",
       "Crime Stoppers          180\n",
       "William Saunders        120\n",
       "Justin Roedel           120\n",
       "Share Copy Link Copy    112\n",
       "Officer Culpepper        90\n",
       "Jessie Lee Williams      82\n",
       "George Floyd             80\n",
       "Graham v. Connor         78\n",
       "Weather Alert            74\n",
       "Circle K                 73\n",
       "Peggy Jo                 72\n",
       "Donna Mills              72\n",
       "Michael Davis            70\n",
       "Sergeant Graham          69\n",
       "Pat Camden               67\n",
       "Crown Victoria           67\n",
       "Donald Smith             67\n",
       "David Baker              67\n",
       "Matthew Casey            66\n",
       "Robert Johnson           66\n",
       "William Kennan           65\n",
       "Officer Smith            65\n",
       "Daytona Beach            64\n",
       "Rudy Eugene              64\n",
       "Richard Williams         64\n",
       "Bobby Earl Driggers      63\n",
       "Ian Burlakoff            63\n",
       "Leslie Vaughn Prater     63\n",
       "Brian Naab               63\n",
       "Della Ventura            63\n",
       "Daniel Jopek             63\n",
       "Frank Jackson            63\n",
       "Jermaine McBean          63\n",
       "Billy Ray Retherford     63\n",
       "Christopher Dolch        63\n",
       "Luis Perez               62\n",
       "Holly Hill               62\n",
       "Kenneth Morrow           62\n",
       "Christopher Greer        62\n",
       "Adam Brown               62\n",
       "John Morgan              62\n",
       "Joshua Cartwright        62\n",
       "Walter Thomas            62\n",
       "Reynaldo Rodriguez       62\n",
       "Altamonte Springs        62\n",
       "Benyamin Yisrael         62\n",
       "Andrea Sheldon           62\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ents_spacy[(ents_spacy.label == 'PERSON') & ents_spacy.text.str.match('.* ')].value_counts('text')[0:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xo40MnKJHWBb"
   },
   "source": [
    "## Rebel Large"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the [Rebel Large](https://huggingface.co/Babelscape/rebel-large) model to try to extract relations from the text.\n",
    "\n",
    "> REBEL(Relation Extraction By End-to-end Language generation) is an autoregressive seq2seq model based on BART (Bidirectional and AutoRegressive Transformers) designed for end-to-end relation extraction. Relation extraction involves extracting relation triplets from raw text, an important task in Information extraction for various applications such as knowledge base population, fact-checking, and other downstream tasks.\n",
    ">\n",
    "> [Understanding knowledge graphs: A key to effective data governance](https://www.leewayhertz.com/knowledge-graph-in-machine-learning/) by Akash Takyar\n",
    "\n",
    "The `extract_relations_from_model_output()` function and `KB` class below for extracting the relations from text are copied from Akash's post.\n",
    "\n",
    "We start by loading the modela dn tokenizer for *rebel-large*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273,
     "referenced_widgets": [
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      "36035fcfe74d465aaca9bc6429bd901a",
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      "3ba427480d524b498eaf1ce0ef32bdf0",
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      "577da6d11309410abcb421fc701e333c",
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      "f19c0fd8912d46c8bbb686a518fd3d64",
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      "2dd42bc6218d406dace97d52eb787624",
      "52278f55335b4670bff2967154b3afed",
      "24cd46f0730c49dd827ded7c2576b99c",
      "658afcb98115441589e0ef7216544f3b",
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      "af65e8b6da00411189861b1ce5c9e6ed",
      "06a55e74be594cb6b4d9253fcd8f511c",
      "da65f2e9eecc4b0683e3fe9f5d692174",
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      "7d8b1461afef4122978d139f869ea61e",
      "7c64af9dff6e4adb984910d28105240b",
      "252730a1898b4b79b8c37a45359c1dc9",
      "c6b2cd7fb84f4ad0ad18f0513b1739ac"
     ]
    },
    "executionInfo": {
     "elapsed": 45200,
     "status": "ok",
     "timestamp": 1689971368484,
     "user": {
      "displayName": "Anabelle Alejandra Colmenares",
      "userId": "17922011685325018307"
     },
     "user_tz": 420
    },
    "id": "W5O9keSJIaZk",
    "outputId": "e79ddd3a-40d9-47cc-ab44-7f841975ebda"
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"Babelscape/rebel-large\")\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(\"Babelscape/rebel-large\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function will parse the transformed text to get the assertion as a data structure. It was pulled directly from Akash's post."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "QAQojbIEHfdg"
   },
   "outputs": [],
   "source": [
    "def extract_relations_from_model_output(text):\n",
    "    relations = []\n",
    "    relation, subject, relation, object_ = '', '', '', ''\n",
    "    text = text.strip()\n",
    "    current = 'x'\n",
    "    text_replaced = text.replace(\"<s>\", \"\").replace(\"<pad>\", \"\").replace(\"</s>\", \"\")\n",
    "    for token in text_replaced.split():\n",
    "        if token == \"<triplet>\":\n",
    "            current = 't'\n",
    "            if relation != '':\n",
    "                relations.append({\n",
    "                    'subject': subject.strip(),\n",
    "                    'predicate': relation.strip(),\n",
    "                    'object': object_.strip()\n",
    "                })\n",
    "                relation = ''\n",
    "            subject = ''\n",
    "        elif token == \"<subj>\":\n",
    "            current = 's'\n",
    "            if relation != '':\n",
    "                relations.append({\n",
    "                    'subject': subject.strip(),\n",
    "                    'predicate': relation.strip(),\n",
    "                    'object': object_.strip()\n",
    "                })\n",
    "            object_ = ''\n",
    "        elif token == \"<obj>\":\n",
    "            current = 'o'\n",
    "            relation = ''\n",
    "        else:\n",
    "            if current == 't':\n",
    "                subject += ' ' + token\n",
    "            elif current == 's':\n",
    "                object_ += ' ' + token\n",
    "            elif current == 'o':\n",
    "                relation += ' ' + token\n",
    "    if subject != '' and relation != '' and object_ != '':\n",
    "        relations.append({\n",
    "            'subject': subject.strip(),\n",
    "            'predicate': relation.strip(),\n",
    "            'object': object_.strip()\n",
    "        })\n",
    "    return relations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `parse_text()` function extracts the relations from a chunk of text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "ftjb7v2fIid5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'subject': 'protest', 'predicate': 'participant', 'object': 'Ed'}, {'subject': 'protest', 'predicate': 'location', 'object': 'market'}, {'subject': 'Ed went to the market', 'predicate': 'instance of', 'object': 'protest'}]\n"
     ]
    }
   ],
   "source": [
    "def parse_text(text):\n",
    "    model_inputs = tokenizer(\n",
    "        text, \n",
    "        max_length=512,\n",
    "        padding=True,\n",
    "        truncation=True,\n",
    "        return_tensors='pt'\n",
    "    )\n",
    "    \n",
    "    logging.info(f\"Num tokens: {len(model_inputs['input_ids'][0])}\")\n",
    "\n",
    "    # Generate\n",
    "    gen_kwargs = {\n",
    "        \"max_length\": 216,\n",
    "        \"length_penalty\": 0,\n",
    "        \"num_beams\": 3,\n",
    "        \"num_return_sequences\": 3\n",
    "    }\n",
    "    \n",
    "    generated_tokens = model.generate(\n",
    "        **model_inputs,\n",
    "        **gen_kwargs,\n",
    "    )\n",
    "    decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=False)\n",
    "\n",
    "    for sentence_pred in decoded_preds:\n",
    "        yield from extract_relations_from_model_output(sentence_pred)\n",
    "\n",
    "print(list(parse_text('Ed went to the market. He saw a protest there.')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZdV2BUrcImDn",
    "outputId": "76135a64-d07f-4905-b250-679ff0d1a92c"
   },
   "source": [
    "And now we step through our text files and generate a separate CSV file for each URL. The CSV file acts as a semaphore to prevent gegeneration if it's already there. This allows you to stop the process and start it again later, as it takes a while."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a285e4184eec40aaacaf7a4441b54fc4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/24386 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for text_file in tqdm(glob('data/*.txt')):\n",
    "    text_file = Path(text_file)\n",
    "    fe_id = text_file.stem\n",
    "\n",
    "    # don't regenerate output if we already have it\n",
    "    csv_file = Path('data') / f\"{fe_id}-rebel.csv\"\n",
    "    if csv_file.is_file():\n",
    "        continue\n",
    "\n",
    "    text = Path(text_file).open().read()\n",
    "    df = pd.DataFrame(list(parse_text(text)))\n",
    "\n",
    "    if len(df) > 0:\n",
    "        logging.info('saving %s assertions to %s', len(df), csv_file)\n",
    "        df['fe_id'] = fe_id\n",
    "        df.to_csv(csv_file, index=False)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can aggregate all the small CSVs into one big CSV of all the assertions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>predicate</th>\n",
       "      <th>object</th>\n",
       "      <th>fe_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>private browsing window</td>\n",
       "      <td>use</td>\n",
       "      <td>sign in</td>\n",
       "      <td>4391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>private browsing window</td>\n",
       "      <td>subclass of</td>\n",
       "      <td>computer</td>\n",
       "      <td>4391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>private browsing window</td>\n",
       "      <td>subclass of</td>\n",
       "      <td>sign in</td>\n",
       "      <td>4391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>located in the administrative territorial entity</td>\n",
       "      <td>Riverside County</td>\n",
       "      <td>13668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Upland</td>\n",
       "      <td>located in the administrative territorial entity</td>\n",
       "      <td>Riverside County</td>\n",
       "      <td>13668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>private browsing window</td>\n",
       "      <td>subclass of</td>\n",
       "      <td>computer</td>\n",
       "      <td>20342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>private browsing window</td>\n",
       "      <td>subclass of</td>\n",
       "      <td>sign in</td>\n",
       "      <td>20342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>version</td>\n",
       "      <td>facet of</td>\n",
       "      <td>browser</td>\n",
       "      <td>4635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>version</td>\n",
       "      <td>subclass of</td>\n",
       "      <td>browser</td>\n",
       "      <td>4635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>version of the browser you are using</td>\n",
       "      <td>subclass of</td>\n",
       "      <td>browser</td>\n",
       "      <td>4635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91298 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 subject  \\\n",
       "0                private browsing window   \n",
       "1                private browsing window   \n",
       "2                private browsing window   \n",
       "0                              Riverside   \n",
       "1                                 Upland   \n",
       "..                                   ...   \n",
       "1                private browsing window   \n",
       "2                private browsing window   \n",
       "0                                version   \n",
       "1                                version   \n",
       "2   version of the browser you are using   \n",
       "\n",
       "                                           predicate            object  fe_id  \n",
       "0                                                use           sign in   4391  \n",
       "1                                        subclass of          computer   4391  \n",
       "2                                        subclass of           sign in   4391  \n",
       "0   located in the administrative territorial entity  Riverside County  13668  \n",
       "1   located in the administrative territorial entity  Riverside County  13668  \n",
       "..                                               ...               ...    ...  \n",
       "1                                        subclass of          computer  20342  \n",
       "2                                        subclass of           sign in  20342  \n",
       "0                                           facet of           browser   4635  \n",
       "1                                        subclass of           browser   4635  \n",
       "2                                        subclass of           browser   4635  \n",
       "\n",
       "[91298 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ents_rebel = pd.concat([\n",
    "    pd.read_csv(csv_file) for csv_file in glob('data/*-rebel.csv')\n",
    "])\n",
    "ents_rebel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject\n",
       "private browsing window                       5811\n",
       "version                                       3730\n",
       "version of the browser you are using          1865\n",
       "John F. Kennedy School of Government          1678\n",
       "World Heritage Site                           1664\n",
       "World War II                                  1664\n",
       "Missouri                                      1003\n",
       "Alabama                                        856\n",
       "Arizona                                        836\n",
       "California                                     827\n",
       "Illinois                                       823\n",
       "New Jersey                                     817\n",
       "Nevada                                         808\n",
       "officer-involved shooting                      510\n",
       "Edit Close                                     462\n",
       "Maryland                                       425\n",
       "New Mexico                                     421\n",
       "New York                                       412\n",
       "Washington D.C.                                401\n",
       "Central African Republic                       336\n",
       "shot and killed                                296\n",
       "{*subject}}                                    294\n",
       "No related articles found for this article     264\n",
       "Breaking News                                  206\n",
       "SWAT                                           180\n",
       "shooting                                       159\n",
       "Police                                         156\n",
       "Jesse Miritello                                156\n",
       "Miami                                          149\n",
       "No related articles                            132\n",
       "LAPD                                           131\n",
       "Kansas                                         125\n",
       "Fort Lauderdale                                123\n",
       "Leah Hogsten                                   122\n",
       "Miami Gardens                                  121\n",
       "fatally shot                                   119\n",
       "Newsletter                                     112\n",
       "Los Angeles Police Department                  106\n",
       "police                                          99\n",
       "email                                           98\n",
       "{*description}}                                 98\n",
       "Police Chief                                    94\n",
       "2020 election results were false                90\n",
       "Advocate Christ Medical Center                  84\n",
       "Oklahoma                                        82\n",
       "Mexico                                          80\n",
       "Virginia                                        78\n",
       "standoff                                        78\n",
       "St. Louis                                       77\n",
       "heat exhaustion                                 74\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 50)\n",
    "\n",
    "ents_rebel.value_counts('subject')[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predicate\n",
       "located in the administrative territorial entity    23964\n",
       "subclass of                                          8676\n",
       "shares border with                                   8327\n",
       "place of death                                       4241\n",
       "applies to jurisdiction                              4174\n",
       "contains administrative territorial entity           3998\n",
       "employer                                             3814\n",
       "location                                             2833\n",
       "part of                                              2707\n",
       "date of death                                        2556\n",
       "use                                                  2472\n",
       "facet of                                             2105\n",
       "point in time                                        1873\n",
       "maintained by                                        1782\n",
       "has effect                                           1760\n",
       "participant                                          1343\n",
       "instance of                                          1291\n",
       "position held                                         943\n",
       "operator                                              842\n",
       "twinned administrative body                           811\n",
       "place of birth                                        739\n",
       "has part                                              691\n",
       "parent organization                                   690\n",
       "capital                                               581\n",
       "headquarters location                                 534\n",
       "owned by                                              493\n",
       "is a list of                                          470\n",
       "country                                               349\n",
       "diplomatic relation                                   338\n",
       "educated at                                           319\n",
       "subsidiary                                            273\n",
       "place of publication                                  273\n",
       "significant event                                     219\n",
       "spouse                                                213\n",
       "officeholder                                          212\n",
       "different from                                        204\n",
       "followed by                                           164\n",
       "member of                                             149\n",
       "school district                                       145\n",
       "main subject                                          140\n",
       "date of birth                                         131\n",
       "capital of                                            124\n",
       "sibling                                               118\n",
       "owner of                                              107\n",
       "position played on team / speciality                  106\n",
       "occupation                                            105\n",
       "located in or next to body of water                   103\n",
       "candidate                                              97\n",
       "inception                                              92\n",
       "head of government                                     79\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ents_rebel.value_counts('predicate')[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object\n",
       "Washington D.C.                    6818\n",
       "browser                            5609\n",
       "sign in                            3874\n",
       "computer                           1937\n",
       "Harvard University                 1675\n",
       "Holocaust                          1664\n",
       "UNESCO                             1664\n",
       "Los Angeles County                  859\n",
       "Chicago                             561\n",
       "Maryland                            449\n",
       "Los Angeles                         351\n",
       "Houston                             329\n",
       "edit                                308\n",
       "Missouri                            305\n",
       "{*description}}                     294\n",
       "Republic of Comoros                 294\n",
       "Brooklyn                            289\n",
       "Texas                               286\n",
       "Phoenix                             273\n",
       "Miami                               269\n",
       "articles found for this article     264\n",
       "Orange County                       261\n",
       "St. Louis                           242\n",
       "Detroit                             233\n",
       "Riverside County                    220\n",
       "Los Angeles Police Department       203\n",
       "Lake County                         185\n",
       "Philadelphia                        176\n",
       "Baltimore                           172\n",
       "San Bernardino                      168\n",
       "San Bernardino County               167\n",
       "officer-involved shooting           165\n",
       "Kansas                              159\n",
       "Jefferson County                    158\n",
       "breaking news                       155\n",
       "edit this article                   154\n",
       "Donald Trump                        144\n",
       "Newark                              144\n",
       "police                              143\n",
       "article                             138\n",
       "Oklahoma City                       138\n",
       "Miami Gardens                       137\n",
       "LAPD                                134\n",
       "Georgia                             134\n",
       "Milwaukee                           133\n",
       "The Salt Lake Tribune               133\n",
       "Cleveland                           130\n",
       "Cook County                         130\n",
       "San Antonio                         128\n",
       "United States of America            126\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ents_rebel.value_counts('object')[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject                                     predicate                                   object                              \n",
       "private browsing window                     subclass of                                 sign in                                 1937\n",
       "                                                                                        computer                                1937\n",
       "                                            use                                         sign in                                 1937\n",
       "version                                     subclass of                                 browser                                 1865\n",
       "version of the browser you are using        subclass of                                 browser                                 1865\n",
       "version                                     facet of                                    browser                                 1865\n",
       "John F. Kennedy School of Government        part of                                     Harvard University                      1675\n",
       "World Heritage Site                         maintained by                               UNESCO                                  1664\n",
       "World War II                                has effect                                  Holocaust                               1664\n",
       "Missouri                                    shares border with                          Washington D.C.                          802\n",
       "Arizona                                     shares border with                          Washington D.C.                          802\n",
       "California                                  shares border with                          Washington D.C.                          802\n",
       "Nevada                                      shares border with                          Washington D.C.                          802\n",
       "Alabama                                     shares border with                          Washington D.C.                          802\n",
       "New Jersey                                  shares border with                          Washington D.C.                          802\n",
       "Illinois                                    shares border with                          Washington D.C.                          802\n",
       "Maryland                                    twinned administrative body                 Washington D.C.                          401\n",
       "New Mexico                                  shares border with                          Washington D.C.                          401\n",
       "Washington D.C.                             twinned administrative body                 Maryland                                 401\n",
       "New York                                    shares border with                          Washington D.C.                          401\n",
       "Central African Republic                    diplomatic relation                         Republic of Comoros                      294\n",
       "{*subject}}                                 part of                                     {*description}}                          196\n",
       "Edit Close                                  use                                         edit                                     154\n",
       "                                                                                        edit this article                        154\n",
       "                                            subclass of                                 edit                                     154\n",
       "No related articles found for this article  is a list of                                article                                  132\n",
       "                                                                                        articles found for this article          132\n",
       "No related articles                         is a list of                                articles found for this article          132\n",
       "Fort Lauderdale                             shares border with                          Miami                                    120\n",
       "{*subject}}                                 followed by                                 {*description}}                           98\n",
       "email                                       has part                                    link                                      98\n",
       "{*description}}                             has part                                    {*subject}}                               98\n",
       "Abby Cunningham                             present in work                             Knots Landing                             72\n",
       "Breaking News                               instance of                                 breaking news coverage                    69\n",
       "                                            main subject                                breaking news                             69\n",
       "                                            instance of                                 breaking news                             65\n",
       "United States Minor Outlying Islands        country                                     United States of America                  63\n",
       "United States of America                    contains administrative territorial entity  United States Minor Outlying Islands      63\n",
       "lawsuit                                     participant                                 Immigration advocates                     63\n",
       "Mexico                                      shares border with                          U.S.                                      63\n",
       "United Mexican States                       country                                     United States of America                  63\n",
       "a lawsuit                                   participant                                 Immigration advocates                     63\n",
       "U.S.                                        shares border with                          Mexico                                    63\n",
       "United Republic of Cape Verde               member of                                   Central African Republic                  63\n",
       "Leah Hogsten                                employer                                    The Salt Lake Tribune                     61\n",
       "                                                                                        Salt Lake Tribune                         61\n",
       "Matthew David Stewart                       employer                                    The Salt Lake Tribune                     61\n",
       "Miami                                       shares border with                          Fort Lauderdale                           60\n",
       "                                                                                        Miami Gardens                             60\n",
       "Miami Gardens                               shares border with                          Miami                                     60\n",
       "Name: count, dtype: int64"
      ]
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