entre.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7b62018-01a6-42a0-bdd2-efbdaa5ba4b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'entity': 'B-LOC', 'score': 0.99975425, 'index': 1, 'word': 'Berlin', 'start': 0, 'end': 6}]\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
    "from transformers import pipeline\n",
    "import joblib\n",
    "import numpy as np\n",
    "import scipy.special as sp\n",
    "from transformers import LukeTokenizer, LukeForEntityPairClassification\n",
    "import json\n",
    "import torch\n",
    "from tqdm import trange\n",
    "import pdb\n",
    "import random\n",
    "from collections import Counter\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"dslim/bert-base-NER\")\n",
    "model = AutoModelForTokenClassification.from_pretrained(\"dslim/bert-base-NER\")\n",
    "\n",
    "nlp = pipeline(\"ner\", model=model, tokenizer=tokenizer, device = 0)\n",
    "example = \"Berlin\"\n",
    "\n",
    "ner_results = nlp(example)\n",
    "print(ner_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8ae343e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function loads the dataset .json file and produce the list containing the relation extraction instances.\n",
    "def load_examples(dataset_file):\n",
    "    with open(dataset_file, \"r\") as f:\n",
    "        data = json.load(f)\n",
    "\n",
    "    examples = []\n",
    "    for i, item in enumerate(data):\n",
    "        tokens = item[\"token\"]\n",
    "        token_spans = dict(\n",
    "            subj=(item[\"subj_start\"], item[\"subj_end\"] + 1),\n",
    "            obj=(item[\"obj_start\"], item[\"obj_end\"] + 1)\n",
    "        )\n",
    "\n",
    "        if token_spans[\"subj\"][0] < token_spans[\"obj\"][0]:\n",
    "            entity_order = (\"subj\", \"obj\")\n",
    "        else:\n",
    "            entity_order = (\"obj\", \"subj\")\n",
    "\n",
    "        text = \"\"\n",
    "        cur = 0\n",
    "        char_spans = {}\n",
    "        for target_entity in entity_order:\n",
    "            token_span = token_spans[target_entity]\n",
    "            text += \" \".join(tokens[cur : token_span[0]])\n",
    "            if text:\n",
    "                text += \" \"\n",
    "            char_start = len(text)\n",
    "            text += \" \".join(tokens[token_span[0] : token_span[1]])\n",
    "            char_end = len(text)\n",
    "            char_spans[target_entity] = (char_start, char_end)\n",
    "            text += \" \"\n",
    "            cur = token_span[1]\n",
    "        text += \" \".join(tokens[cur:])\n",
    "        text = text.rstrip()\n",
    "\n",
    "        examples.append(dict(\n",
    "            text=text,\n",
    "            entity_spans=[tuple(char_spans[\"subj\"]), tuple(char_spans[\"obj\"])],\n",
    "            label=item[\"relation\"],\n",
    "            entity_type = (item['subj_type'], item['obj_type']),\n",
    "        ))\n",
    "\n",
    "    return examples\n",
    "\n",
    "def overlap(range_a, range_ls):\n",
    "    for range_ in range_ls:\n",
    "        if range_[0] <= range_a[0] <= range_[1] or range_a[0] <= range_[0] <= range_a[1]:\n",
    "            return True\n",
    "    \n",
    "    return False\n",
    "\n",
    "def printREInstance(text, subj_span, obj_span, subj_type, obj_type):\n",
    "    if subj_span[0] < obj_span[0]:\n",
    "        new_text = text[:subj_span[0]] + '@' + text[subj_span[0] : subj_span[1]] + '@' + '(subj type:' + subj_type + ')' + \\\n",
    "                    text[subj_span[1] : obj_span[0]] + '#' + text[obj_span[0] : obj_span[1]] + '#' + '(obj type:' + obj_type + ')' + \\\n",
    "                    text[obj_span[1] : ]\n",
    "    else:\n",
    "        new_text = text[:obj_span[0]] + '#' + text[obj_span[0] : obj_span[1]] + '#'  + '(obj type:' + obj_type + ')' + \\\n",
    "                    text[obj_span[1] : subj_span[0]] + '@' + text[subj_span[0] : subj_span[1]] + '@' + '(subj type:' + subj_type + ')' + \\\n",
    "                    text[subj_span[1] : ]\n",
    "        \n",
    "    print(new_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "feb34b72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "He has served as a policy aide to the late U.S. Senator Alan Cranston , as National Issues Director for the 2004 presidential campaign of Congressman Dennis Kucinich , as a co-founder of @Progressive Democrats of America@(subj type:ORGANIZATION) and as a member of the #international policy department#(obj type:ORGANIZATION) at the RAND Corporation think tank before all that .\n",
      "distinct number of tacred subj person names:  238\n",
      "distinct number of tacred subj organization names:  204\n",
      "distinct number of retacred subj person names:  217\n",
      "distinct number of retacred subj organization names:  177\n",
      "distinct number of tacrev subj person names:  238\n",
      "distinct number of tacrev subj organization names:  204\n"
     ]
    }
   ],
   "source": [
    "test_examples = load_examples(\"test.json\")\n",
    "re_test_examples = load_examples(\"test_re.json\")\n",
    "rev_test_examples = load_examples(\"test_rev.json\")\n",
    "printREInstance(test_examples[0]['text'], \n",
    "                test_examples[0]['entity_spans'][0], test_examples[0]['entity_spans'][1],\n",
    "                test_examples[0]['entity_type'][0], test_examples[0]['entity_type'][1],\n",
    "               )\n",
    "\n",
    "subj_per_name_ls = list(\n",
    "        [\n",
    "        test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][0][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][0][1]\n",
    "            ].upper()\n",
    "        for id_ in range(len(test_examples))\n",
    "        if \n",
    "        (test_examples[id_]['entity_type'][0] == 'PERSON'\n",
    "        )\n",
    "        ]\n",
    ")\n",
    "\n",
    "subj_org_name_ls = list(\n",
    "        [\n",
    "        test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][0][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][0][1]\n",
    "            ].upper()\n",
    "        for id_ in range(len(test_examples))\n",
    "        if \n",
    "        (test_examples[id_]['entity_type'][0] == 'ORGANIZATION'\n",
    "        )\n",
    "        ]\n",
    ")\n",
    "\n",
    "print('distinct number of tacred subj person names: ', len(set(subj_per_name_ls)))\n",
    "print('distinct number of tacred subj organization names: ',len(set(subj_org_name_ls)))\n",
    "\n",
    "re_subj_per_name_ls = list(\n",
    "        [\n",
    "        re_test_examples[id_]['text'][\n",
    "            re_test_examples[id_]['entity_spans'][0][0] \n",
    "            : \n",
    "            re_test_examples[id_]['entity_spans'][0][1]\n",
    "            ].upper()\n",
    "        for id_ in range(len(re_test_examples))\n",
    "        if \n",
    "        (re_test_examples[id_]['entity_type'][0] == 'PERSON'\n",
    "        )\n",
    "        ]\n",
    ")\n",
    "\n",
    "re_subj_org_name_ls = list(\n",
    "        [\n",
    "        re_test_examples[id_]['text'][\n",
    "            re_test_examples[id_]['entity_spans'][0][0] \n",
    "            : \n",
    "            re_test_examples[id_]['entity_spans'][0][1]\n",
    "            ].upper()\n",
    "        for id_ in range(len(re_test_examples))\n",
    "        if \n",
    "        (re_test_examples[id_]['entity_type'][0] == 'ORGANIZATION'\n",
    "        )\n",
    "        ]\n",
    ")\n",
    "\n",
    "print('distinct number of retacred subj person names: ', len(set(re_subj_per_name_ls)))\n",
    "print('distinct number of retacred subj organization names: ',len(set(re_subj_org_name_ls)))\n",
    "\n",
    "rev_subj_per_name_ls = list(\n",
    "        [\n",
    "        rev_test_examples[id_]['text'][\n",
    "            rev_test_examples[id_]['entity_spans'][0][0] \n",
    "            : \n",
    "            rev_test_examples[id_]['entity_spans'][0][1]\n",
    "            ].upper()\n",
    "        for id_ in range(len(rev_test_examples))\n",
    "        if \n",
    "        (rev_test_examples[id_]['entity_type'][0] == 'PERSON'\n",
    "        )\n",
    "        ]\n",
    ")\n",
    "\n",
    "rev_subj_org_name_ls = list(\n",
    "        [\n",
    "        rev_test_examples[id_]['text'][\n",
    "            rev_test_examples[id_]['entity_spans'][0][0] \n",
    "            : \n",
    "            rev_test_examples[id_]['entity_spans'][0][1]\n",
    "            ].upper()\n",
    "        for id_ in range(len(rev_test_examples))\n",
    "        if \n",
    "        (rev_test_examples[id_]['entity_type'][0] == 'ORGANIZATION'\n",
    "        )\n",
    "        ]\n",
    ")\n",
    "\n",
    "print('distinct number of tacrev subj person names: ', len(set(rev_subj_per_name_ls)))\n",
    "print('distinct number of tacrev subj organization names: ',len(set(rev_subj_org_name_ls)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9a92830a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 122/122 [03:21<00:00,  1.66s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10145 15509\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 122/122 [03:21<00:00,  1.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4912 15509\n",
      "11661 15509\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "subj_overlap_ls = []\n",
    "batch_size = 128\n",
    "for batch_start_idx in trange(0, len(test_examples), batch_size):\n",
    "    batch_examples = test_examples[batch_start_idx:batch_start_idx + batch_size]\n",
    "    texts = [example[\"text\"] for example in batch_examples]\n",
    "    entity_spans = [example[\"entity_spans\"][0] for example in batch_examples]\n",
    "    entity_types = [example[\"entity_type\"][0] for example in batch_examples]\n",
    "    \n",
    "    ner_result_ls = nlp(texts)\n",
    "    ner_per_ls = [[(ner_['start'], ner_['end']) for ner_ in entity_ls_ if 'PER' in ner_['entity']] for entity_ls_ in ner_result_ls]\n",
    "    ner_org_ls = [[(ner_['start'], ner_['end']) for ner_ in entity_ls_ if 'ORG' in ner_['entity']] for entity_ls_ in ner_result_ls]\n",
    "\n",
    "    overlap_flag = [\n",
    "        False\n",
    "        or (overlap(entity_spans[i_], ner_per_ls[i_]) and entity_types[i_] == 'PERSON')\n",
    "        or (overlap(entity_spans[i_], ner_org_ls[i_]) and entity_types[i_] == 'ORGANIZATION')\n",
    "        for i_ in range(len(batch_examples))\n",
    "    ]\n",
    "    \n",
    "    subj_overlap_ls.extend([i_ + batch_start_idx for i_ in range(len(batch_examples)) if overlap_flag[i_]])\n",
    "\n",
    "print(len(subj_overlap_ls), len(test_examples))\n",
    "\n",
    "obj_overlap_ls = []\n",
    "for batch_start_idx in trange(0, len(test_examples), batch_size):\n",
    "    batch_examples = test_examples[batch_start_idx:batch_start_idx + batch_size]\n",
    "    texts = [example[\"text\"] for example in batch_examples]\n",
    "    entity_spans = [example[\"entity_spans\"][1] for example in batch_examples]\n",
    "    entity_types = [example[\"entity_type\"][1] for example in batch_examples]\n",
    "    \n",
    "    ner_result_ls = nlp(texts)\n",
    "    ner_per_ls = [[(ner_['start'], ner_['end']) for ner_ in entity_ls_ if 'PER' in ner_['entity']] for entity_ls_ in ner_result_ls]\n",
    "    ner_org_ls = [[(ner_['start'], ner_['end']) for ner_ in entity_ls_ if 'ORG' in ner_['entity']] for entity_ls_ in ner_result_ls]\n",
    "\n",
    "    overlap_flag = [\n",
    "        False\n",
    "        or (overlap(entity_spans[i_], ner_per_ls[i_]) and entity_types[i_] == 'PERSON')\n",
    "        or (overlap(entity_spans[i_], ner_org_ls[i_]) and entity_types[i_] == 'ORGANIZATION')\n",
    "        for i_ in range(len(batch_examples))\n",
    "    ]\n",
    "    \n",
    "    obj_overlap_ls.extend([i_ + batch_start_idx for i_ in range(len(batch_examples)) if overlap_flag[i_]])\n",
    "\n",
    "print(len(obj_overlap_ls), len(test_examples))\n",
    "print(len(set(obj_overlap_ls).union(set(subj_overlap_ls))), len(test_examples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "edb485a6",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of distinct named organization:  966\n",
      "counter of named organization:  Counter({'CORPORATE LIBRARY': 243, 'SHOPPERTRAK': 240, 'ALICO': 236, 'ADF': 215, 'NATIONAL RESTAURANT ASSOCIATION': 212, 'PATA': 194, 'COUNTRYWIDE': 192, 'NATIONAL URBAN LEAGUE': 179, 'CHINA CHARITY FEDERATION': 167, 'NATIONAL DEVELOPMENT AND REFORM COMMISSION': 166, 'NDRC': 128, 'PACIFIC ASIA TRAVEL ASSOCIATION': 122, 'NATIONAL CONGRESS OF AMERICAN INDIANS': 118, 'PROGRESSIVE DEMOCRATS OF AMERICA': 102, 'OANA': 100, 'NATIONAL TAIWAN SYMPHONY ORCHESTRA': 99, 'NATIONAL ENERGY ADMINISTRATION': 94, 'AMERICAN FREE PRESS': 93, 'TPI': 93, 'UASR': 85, 'LIHOP': 82, 'MENIL COLLECTION': 81, 'NDA': 81, 'SYCAMORE': 77, 'NTSO': 75, 'PDA': 71, 'URBAN LEAGUE': 66, 'SEMEN GRESIK': 63, 'AFP': 56, 'PSIA': 47, 'LOOKING CLOUD': 43, 'KOCH FOODS': 43, 'COUNTRYWIDE FINANCIAL': 42, 'AAUW': 39, 'COUNTRYWIDE FINANCIAL CORP.': 38, 'NRA': 36, 'NUCLEAR DECOMMISSIONING AUTHORITY': 36, 'AIG': 36, 'TRIBECA ENTERPRISES': 34, 'AMERICAN ASSOCIATION OF UNIVERSITY WOMEN': 33, 'NEW FABRIS': 29, 'NEA': 29, 'HKI': 29, 'LOOSE CHANGE': 28, 'CCF': 27, 'UN': 27, 'AUSTRALIAN DEFENCE FORCE': 26, 'HELEN KELLER INTERNATIONAL': 25, 'MEF': 25, 'METLIFE': 24, 'ORGANIZATION OF ASIA-PACIFIC NEWS AGENCIES': 23, 'KOCH FOODS INC.': 23, 'AIA': 22, 'AUSTRALIAN DEFENSE FORCE': 21, 'MYANMAR EQUESTRIAN FEDERATION': 21, 'UNITED NATIONS': 18, 'CHONGQING MUNICIPAL JUDICIAL BUREAU': 18, 'COASTAL CONSERVATION LEAGUE': 17, 'DEFENCE FORCE': 17, 'PROFESSIONAL SKI INSTRUCTORS OF AMERICA': 16, 'NCAI': 16, 'AMERICAN LIFE INSURANCE COMPANY': 16, 'ORGANIZATION OF ASIA PACIFIC NEWS AGENCIES': 15, 'RED CROSS SOCIETY OF CHINA': 15, 'NAACP': 15, 'RED CROSS': 15, 'ORGANISATION OF ASIA-PACIFIC NEWS AGENCIES': 14, 'HELOISE': 14, 'MIHOP': 14, 'UNITED ASSOCIATION FOR STUDIES AND RESEARCH': 13, 'HOLLISTER INC.': 13, 'U.N.': 13, 'CONGRESS': 13, 'THIRD GUARD DIVISION': 12, 'BANK OF AMERICA': 12, 'PLURIBUS CAPITAL MANAGEMENT': 11, 'SAP': 11, 'TAIWAN SYMPHONY ORCHESTRA': 11, 'KBR': 10, 'SEC': 10, 'THE CHINA CHARITY FEDERATION': 9, 'THE MENIL COLLECTION': 9, 'CENTRAL AMERICAN PARLIAMENT': 9, 'CUSTOMS ENFORCEMENT': 9, 'ORACLE': 9, 'STATE DEPARTMENT': 9, 'TALIBAN': 9, 'HAMAS': 9, 'LC': 8, 'ORGANIZATION OF ASIA-PACIFIC NEWS AGENCY': 8, 'THAI PETROCHEMICAL INDUSTRY PLC': 8, 'UNITED SOLUTIONS AND SERVICES': 8, 'ACSE': 8, 'UNITED NEGRO COLLEGE FUND': 8, 'UNWTO': 8, 'XINHUA': 8, 'MGE': 8, 'FBI': 8, 'AMERICAN LIFE INSURANCE CO.': 8, 'TEMPAT PELELANGAN IKAN': 7, 'COUNTRYWIDE FINANCIAL CORPORATION': 7, 'POVERTY AND SOCIAL IMPACT ANALYSIS OF REFORMS': 7, 'SECURITY COUNCIL': 7, 'INTERNATIONAL COMMITTEE OF THE RED CROSS': 7, 'IRS': 7, 'AP': 7, 'LA LINEA': 7, 'THE NATIONAL RESTAURANT ASSOCIATION': 6, 'PACIFIC-ASIA TRAVEL ASSOCIATION': 6, 'COUNTRYWIDE FINANCIAL CORP': 6, 'QATALYS , INC': 6, 'MEDIO ORIENTE NIR ROSEN': 6, 'COUNCIL FOR CULTURAL AFFAIRS': 6, 'CIT': 6, 'MCA': 6, 'RCSC': 6, 'HALLIBURTON': 6, 'WORLD BANK': 6, 'HOUSE': 6, 'PACKARD': 6, 'AMERICAN LIFE INSURANCE CO': 6, 'UNIVERSITY OF CALGARY': 6, 'YOUNG PATRIOTS': 6, 'CEPHALON': 6, 'AMERICAN INTERNATIONAL GROUP': 6, 'COUNTRYWIDE HOME LOANS': 5, \"GAY MEN 'S HEALTH CRISIS\": 5, 'PLURIBUS': 5, 'SYCAMORE SOFTWARE SOLUTIONS': 5, 'CFC': 5, 'PARLACEN': 5, 'ALLIED DEMOCRATIC FORCES': 5, 'TPIPL': 5, 'POPULAR MECHANICS': 5, 'INTERNATIONAL COMMITTEE': 5, 'BANK OF AMERICA CORP.': 5, 'NYU': 5, \"NEW LIFE CHILDREN 'S REFUGE\": 5, 'TIMES': 5, 'CHURCH OF SCIENTOLOGY': 5, 'ICRC': 5, 'NEW YORK TIMES': 5, 'MUSEUM OF MODERN ART': 5, 'BBC': 5, \"INTERMEDIATE PEOPLE 'S COURT\": 5, 'UNIVERSITY OF WASHINGTON': 5, 'TRUTH MOVEMENT': 5, '`` YOUNG PATRIOTS': 5, 'COUNTRYWIDE BANK': 4, 'ORGANIZATION OF ASIA - 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      "number of distinct named person:  1257\n",
      "counter of named person:  Counter({'BIBI': 230, 'GRAHAM': 215, 'CHEN TAO': 212, 'AHMED RASHID': 183, 'WEN QIANG': 182, 'NIR ROSEN': 177, 'MARK FISHER': 166, 'PIEDRA': 143, 'SAMUDIO': 143, 'RICHARD GAGE': 137, 'CHARLES BLE GOUDE': 129, 'RUBEN VAN ASSOUW': 127, 'VAGNI': 124, 'ALAN GROSS': 121, 'XIE CAIPING': 119, 'AQUASH': 112, 'MATTEO MESSINA DENARO': 104, 'EUGENIO VAGNI': 103, 'LAURA SILSBY': 100, 'ALESSI': 100, 'SHAH ABDUL AZIZ': 99, 'CERNIGLIA': 97, 'YOUSSOUFOU BAMBA': 96, 'JOHN GRAHAM': 92, 'JAMIE LEIGH JONES': 91, 'BLE GOUDE': 87, 'CHIAFARI': 85, 'CHRISTOPHER BOLLYN': 83, 'HELOISE': 82, 'RASHID': 77, 'SARAH KUNSTLER': 73, 'KNOX': 71, 'BENJAMIN CHERTOFF': 69, 'WEN': 68, 'DWORIN': 66, 'AMANDA KNOX': 64, 'ADELA DWORIN': 63, 'DANIELS': 62, 'ELIZA SAMUDIO': 61, 'GONZALEZ': 60, 'ILAN RAMON': 57, 'MARIO GONZALEZ': 57, 'VLADIMIR LADYZHENSKIY': 56, 'HAGEMO': 56, 'LOMAX': 54, 'MICHAEL LOMAX': 53, 'JUPP': 50, 'CRAIG RANKE': 49, 'MEREDITH KERCHER': 43, 'ELOISE SPOONER': 40, 'SODINI': 40, 'SANDRA HEROLD': 39, 'CAROL DANIELS': 38, 'NORRIS CHURCH MAILER': 38, 'NASH': 38, 'GEORGE SODINI': 37, 'NELL MINOW': 37, 'WAYNE A. 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MYERS': 1, 'ALDO': 1, 'NATHAN MERRICK': 1, 'JAMES P. TUCKER JR.': 1, 'WALKER': 1, 'CONDOLEEZZA RICE': 1, 'HANS HOFMANN': 1, 'BRUNO RODRIGUEZ': 1, 'JOHN TRUDELL': 1, 'ESMAEL ADIBI': 1, 'DORGAN': 1, 'PETER ALLEN': 1, 'DARYL BRADFORD SMITH': 1, 'DARCY SWEENEY': 1, 'MICHAEL HEIZER': 1, 'FRANCESCO MASCINI': 1, 'NUR-ANA SAHIDULLA': 1, 'STEFANO GIOVANNONI': 1, 'JEREMY HASLAM': 1, 'TERRY SILSBY': 1, 'BUNDT': 1, 'ANNALISA BARRETT': 1, 'YU': 1, 'BREITBART': 1, 'LI YE': 1, 'DARNELL L. WILLIAMS': 1, 'JOHN BALDESSARI': 1, 'STEVEN PETRARCA': 1, 'WOOTAN': 1, 'LINARES': 1, 'DAVID ACHEY': 1, 'JOHANNES BRAHMS': 1, 'CHARLES PRINCE': 1, 'DU WEI': 1, 'JOE SUNDERMAN': 1, 'AHMED YOUSEF': 1, 'MARJORIE J HILL': 1, 'WILLIAM A LANGE': 1, 'DENNIS KUCINICH': 1, 'JOSE MILLER': 1, 'NICO': 1, 'ANNE': 1, 'ORLY L. MALDONADO': 1, 'SAMUELS': 1, 'KATAMON KONNECTIONS': 1, 'BWEBRE': 1, 'REDFORD': 1, 'CHRISTOPHER J. SWIFT': 1, 'ZENG CHENG': 1, 'YISRAEL': 1, 'FRANCESCO SOLLECITO': 1, 'RICHARDSON': 1, 'PATRICK VAN ASSOUW': 1, 'MARTI GUIX': 1, 'WILLIAM WOOD': 1, 'BUT ZHANG GUOBAO': 1, 'ALTERNET': 1, 'JEAN LACABA': 1, 'MARC H MORIAL': 1, 'EMMANUEL PAHUD': 1, 'MIKE BLAIR': 1, 'GREEN': 1, 'CHEN YUAN': 1, 'ROSENBERGER': 1, 'FRANCO': 1, 'HONG YU-JING': 1, 'JIM': 1, 'KARIM RASHID': 1, 'ANTONY': 1, 'MAHMOUD AHMADINEJAD': 1, 'SCHULTZ': 1, 'DIANE REHM': 1, 'MELISSA': 1, 'JIANG YIMAN': 1, 'MAURICIO': 1, 'FRED': 1, 'FERGUSON': 1, 'ANNA DICHTER': 1, 'DOHA CATHERINE UGWU': 1, 'BETTY': 1, 'ROSA HERNANDEZ': 1, 'DVORAK': 1, 'WILLIAM F. BUCKLEY JR.': 1, 'TERRY JEMISON': 1, 'LEONARD PELTIER': 1, 'JAMES DAVIS': 1, 'NORTON': 1, 'LOU GEHRIG': 1, 'ALFONSO MALDONADO': 1, 'BOLANOS': 1, \"O'REILLY\": 1, 'ABU NIDAL': 1, 'KILLINGER': 1, 'ALAIN AESCHLIMANN': 1, 'DON JASON': 1, 'LARRY KRAMER': 1, 'PETER SEMONE': 1, 'ROY': 1, 'MARGARET GARRITSEN': 1, 'JOCK MCTAVISH': 1, 'YARDENI': 1, 'JOHN EDWARDS': 1, 'NETEMARA': 1, 'CALVIN MOORE': 1, 'RUDOLPH GIULIANI': 1, 'PAUL J. MILLER JR.': 1, 'HE YAFEI': 1, 'LIU JIANYE': 1, 'SOUSA': 1, 'WILLIE BROWN': 1, 'MARTIN KOUAKOU FOFIE': 1, 'STEPHEN TABOR': 1, 'YOUSEF QURESHI': 1, 'JOE GARCIA': 1, 'MICHAEL RUPPERT': 1, 'VIK MUNIZ': 1, 'SALIM': 1, 'JOHN ARMSTRONG': 1, 'DANIEL E. SMITH': 1, 'TAMAS VASARY': 1, 'BARDWELL': 1, 'OGBU': 1, 'ABDUSAKUR TAN': 1, 'DAVID ZEIDBERG': 1, 'ARTHUR DICHTER': 1, 'BILL DOYLE': 1, 'BRUNO FERNANDA GOMES DE CASTRO': 1, 'MCCABE': 1, 'BILLIE WHITELAW': 1, 'KARGMAN': 1, 'GIOVANNONI': 1, 'DAVID ICKE': 1, 'RATHBUN': 1, 'MORENO-OCAMPO': 1, 'GRACE BRUGESS': 1, 'KRUKOWICZ': 1, 'HOLMES': 1, 'JASON CLARE': 1, 'KEVIN FEDERLINE': 1, 'WANG SIQIANG': 1, 'JON PATRICOF': 1, 'KEN SIPE': 1, 'DAVID LOEB': 1, 'ZHAO PENG': 1, 'HONG HUANG': 1, 'ARLO': 1, 'PUNO': 1, 'SAINVIL': 1, 'BERNE': 1, 'LANKFORD': 1, 'SORO': 1, 'PHILIP CROWLEY': 1, 'YUSUF': 1, 'BRIAN DEESON': 1, 'YAN XIANGCHUANG': 1, 'CISNEROS': 1, 'ROBERT BENMOSCHE': 1, 'DAVID RAY GRIFFIN HE': 1, 'CARLO DALLA VEDOVA': 1, 'DAVIES': 1, 'JULIE JONES': 1, 'ROBERT DE NIRO': 1, 'MICHELE WETHERALD': 1, 'BERNICE BEREND ROSE': 1, 'HANK HEI': 1, 'SISTER-IN-LAW , XIE': 1, 'DJUE': 1, 'DENNY MCGUIRE': 1, 'QIAN ZHIMIN': 1, 'STEPANEK': 1, 'TOBIE': 1, 'BETH M. YOUNG': 1, 'CURT WELDON': 1, \"O'GRADY\": 1, 'PEREIRA': 1, 'ZHANG': 1, 'ADAM PRICE': 1, 'ZHANG PING': 1, 'JOHN DEWAR': 1, 'HE': 1, 'SUNNY PEREIRA': 1, 'FRATTINI': 1, 'FRANCESCO COSSIGA': 1, 'KENT CROFT': 1, 'JORGE ANIBAL TORRES PUELLO': 1, 'MEREDITH': 1, 'J. MARX AYRES': 1, 'JUSUF KALLA': 1, 'DANIELLE': 1, 'ERIC P SIERACKI': 1, 'ELIEZER SHKEIDI': 1, 'JIM FETZER': 1, 'MARTIN LUTHER KING III': 1, 'ZHOU': 1, 'TERESA PRINCIPATO': 1, 'PIOTR PASZKOWSKI': 1, 'KARSNER': 1, 'MIKE GRAHAM': 1, 'JACQUELINE L. PATA': 1, 'QASSIM HAMZA RAHEEM': 1, 'REZA RAHMAN': 1, 'GERALD W. MCENTEE': 1, 'EMERY PATRICE BOSSO': 1, 'LUO XUAN': 1, 'GIUSEPPE LINARES': 1, 'LUIZ HENRIQUE ROMAO': 1, 'MICHAEL GIBBONS': 1, 'SHAHBAZ BHATTI': 1, 'KHALID AL-MIHDHAR': 1, 'DWORINBS': 1, 'NIDAL': 1, 'JIM HIGHTOWER': 1, 'LOCKEMY': 1, 'JAMAIL': 1, 'KEVIN RYAN': 1, 'MUSHARRAF': 1, 'DEBBIE MALONEY PICTOU': 1, 'BARBARA JEAN DAVIS': 1, 'AMANPOUR': 1, \"DANNY O'BRIEN\": 1, 'DEBORAH MIDELTON': 1, 'SINGH': 1, 'DWIGHT GARNER': 1, 'JAMIE': 1, 'ALI MOKHTARE': 1, 'BEERE': 1, 'FOX': 1, 'KEVIN BARRETT': 1, 'BEETHOVEN': 1, 'NOTTER': 1, 'BENEDICT XVI': 1, 'WANG TIANLUN': 1, 'JOHN MCCAIN': 1, 'ANGELO R. MOZILO': 1, 'MARJORIE HILL': 1, 'MAL BROUGH': 1, 'ANDREW MORRIS': 1, 'CECALA': 1, 'GEORGE WILLEIT': 1, 'MARIE ANTOINETTE': 1, 'STEPHANIE J JONES': 1, 'ELIZABETH': 1, 'CRUSE': 1, 'JOSEPH NUSAIRAT': 1, 'TSAI CHIH-KUE': 1, 'DEANNA': 1, 'STEVE COBBLE': 1, 'MOUSA ABU MAZOOK': 1, 'SUNDER': 1, 'MICHAEL MOORES': 1, 'MARVIN BUSH': 1, 'MARK THOMAS': 1, 'PERRELLI': 1, 'FEDERICO LOMBARDI': 1, 'CAMNER': 1, 'MARK FISCHER': 1, 'FARRELL': 1, 'FITZGIBBON': 1, 'R. `` WILLIAM KUNSTLER': 1, 'WU GUIHUI': 1, 'RONALD ROSS': 1, 'BACON': 1, 'MA SHUI-LONG': 1, 'CRAIG HATKOFF': 1, 'PEARSON': 1, 'DALE LAWRENCE': 1, 'GREGORYULRICH': 1, 'MICHAEL BERNARD MUKASEY': 1, 'MICHIHIRO YASUDA': 1, 'FOXY KNOXY': 1, 'DIYA LUMUMBA': 1, 'ED `` LOU GRANT': 1, 'QIAN LONG': 1, 'BARB WONG': 1, 'ZHANG XIAOQIANG': 1, 'EZRA RANDLE': 1, 'GEORGE PACKER': 1, 'JOHN LEWIS': 1, 'MUKULU': 1, 'WHITECROSS': 1, 'MATTHEW DOULL': 1, 'JEAN-LOU CHAMEAU': 1, 'BARBARA HONEGGER': 1, 'WANG YIMING': 1, 'HENRY HUNTINGTON': 1, 'SAIF AL-ISLAM': 1, 'PONCE KIAH MARCHELLE HELOISE CRUSE EVANS': 1, 'KEVIN GREGG': 1, 'MOFFATT': 1, 'ARNON MILCHAN': 1, 'ANTARA AHMAD MUKHLIS YUSUF': 1, 'CONNOR': 1, 'TAGRO': 1, 'HAN YONGWEN': 1, 'MARION COTILLARD': 1, 'COLONEL ILAN RAMON': 1, 'JAMES MARTIN MURPHY': 1, 'CERMINARA': 1, 'GAYNELL DAVIS': 1, 'BOOBIE': 1, 'PANG CHENMIN': 1, 'JEREMY CORBYN': 1, 'HEIDI UEBERROTH': 1, 'DARNELL WILLIAMS': 1, 'MICK HUFF': 1, 'ROBIN HERMAN': 1, 'DAN': 1, 'MADDOX': 1, 'R.D.': 1, 'SUE LAFFERTY': 1, 'MEIGS': 1, 'WILKE': 1, 'STEPHEN SCHWARZMAN': 1, 'KOESTEY': 1, 'JERO WATJIK': 1, 'STEVEN C. ANDERSON': 1, 'CLAUDIE': 1, 'WILLIAM RODRIGUEZ': 1, 'CARLOS NOGUEIRA': 1, 'HEIKO MATHIAS FOERSTER': 1, 'AMY GOODMAN': 1, 'JEREMY': 1, 'WINANS': 1, 'BRENDAN FLANAGAN': 1, 'BARBARA WASSERMAN': 1, 'LARA BURNS': 1, 'MARLENE DUMAS': 1, 'VAN TOL': 1, 'KONIN': 1, 'BENTLEY': 1, 'LAURENT KABILA': 1, 'PAUL SIMON': 1, 'EDWARD LIDDY': 1, 'BARRY MUKAMAL': 1, 'AZMAN UJANG': 1, 'TOM': 1, 'DAN A. BAILEY JR.': 1, 'DAVID PRICE': 1, 'HANS DOCKTER': 1, 'CROWLEY': 1, 'BAOJUN': 1, 'WILLIAM KNOX': 1, 'CHRIS MOYER': 1, 'MR SCHEUERMANN': 1, 'CARLA COOLMAN': 1, 'BAILEY': 1, 'DOTTIE': 1, 'MICHAEL C GIBBONS': 1, 'JOSEPH P. CONWAY': 1, 'LUCILLE BALL': 1, 'CAROLE MALLORY': 1, 'RONG HAO': 1, 'MARK ROBERTS': 1, 'CLINTON': 1, 'MUDD': 1, 'MARCOS APARECIDO SANTOS': 1, 'PHILIPPE STARCK': 1, 'FRANK BLAKE': 1, 'PENG': 1, 'LORNA LATHAM': 1, 'BOLLYN': 1, 'SHORT': 1, 'FILIPINA MARY JEAN LACABA': 1, 'HUGH': 1, 'PETER': 1, 'GERALD A. FERNANDEZ': 1, 'GEORGE H.W. BUSH': 1, 'LUKE': 1, 'JACKSON SLIM BROSSY': 1, 'CHARLA': 1, 'ROBERT PAPE': 1, 'JOHN PAUL II': 1, 'SYRON': 1, 'ROY WILKINS': 1, 'JULIE DARDANO': 1, 'LISA': 1, 'NIDIA DIAZ': 1, 'MICHAEL COLLINS PIPER': 1, 'BENNETT': 1, 'FESSLER': 1, 'OLIVER STONE': 1, 'JOHN BUFFALO': 1, 'PAUL HODGSEN': 1, 'SHELBY LAFRENIERE': 1, 'ALBERT': 1, 'GOBER': 1, 'CLEOPATRA': 1, 'SALMAAN TASEER': 1, 'WANG HUNING': 1, 'RON PAUL': 1, 'MIGNINI': 1, 'EUGENIO CLEMEN': 1, 'NIXON': 1, 'JUERGEN HOELLER': 1, 'STEWIE': 1})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "named_organization_name_ls = [\n",
    "                test_examples[id_]['text'][\n",
    "                    test_examples[id_]['entity_spans'][0][0] \n",
    "                    : \n",
    "                    test_examples[id_]['entity_spans'][0][1]\n",
    "                    ].upper()\n",
    "                for id_ in subj_overlap_ls\n",
    "                if test_examples[id_]['entity_type'][0] == 'ORGANIZATION'\n",
    "                ] + \\\n",
    "                [\n",
    "                test_examples[id_]['text'][\n",
    "                    test_examples[id_]['entity_spans'][1][0] \n",
    "                    : \n",
    "                    test_examples[id_]['entity_spans'][1][1]\n",
    "                    ].upper()\n",
    "                for id_ in obj_overlap_ls\n",
    "                if test_examples[id_]['entity_type'][1] == 'ORGANIZATION'\n",
    "                ]\n",
    "\n",
    "print('number of distinct named organization: ', len(set(named_organization_name_ls)))\n",
    "print('counter of named organization: ', Counter(named_organization_name_ls))\n",
    "print()\n",
    "\n",
    "named_person_name_ls = [\n",
    "        test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][0][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][0][1]\n",
    "            ].upper()\n",
    "        for id_ in subj_overlap_ls\n",
    "        if test_examples[id_]['entity_type'][0] == 'PERSON'\n",
    "        ] + \\\n",
    "        [\n",
    "        test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][1][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][1][1]\n",
    "            ].upper()\n",
    "        for id_ in obj_overlap_ls\n",
    "        if test_examples[id_]['entity_type'][1] == 'PERSON'\n",
    "        ]\n",
    "\n",
    "\n",
    "print('number of distinct named person: ', len(set(named_person_name_ls)))\n",
    "print('counter of named person: ', Counter(named_person_name_ls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3a2c5b6d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the bad person entities that cannot be replaced with other persons:  ['HELPFUL HINTER', 'SHE', 'BURNS', 'BOFA', 'CASTRO', 'KALINEN', 'JUAREZ', 'ST. PAUL', 'SEARS', 'A SENIOR OFFICIAL', 'CREEK', 'LENOIR', 'PANEER BURJI', 'AWAITING THE VERDICT WITH OTHER FAMILY MEMBERS', \"''\", 'FRANKO PIZZA', 'TOM HAYDEN', 'PAKISTAN', 'THE BOY', 'DE MENILS', 'NIDAL', 'LEVY', 'KNOX', 'AL-QADA', 'HELL', 'ARMY OF THE MAHDI', 'VICTOR', 'MAN', 'KOBE', 'HER HUSBAND', 'TRAPANI', 'WOMAN', 'MASCARA', 'BERMAS', 'BABY', 'LOUIS GHOST', 'DEPUTY POLICE CHIEF', 'THE KILLER', 'HUSBAND', 'SON CITGO', 'PAUL', 'BELO HORIZONTE', 'REDSTONE', 'NASSER', 'PIHAKNYA SELAKU PENYELENGGARA MENGAKU BANGGA BISA MELAHIRKAN BINTANG-BINTANG MASA DEPAN DARI KALANGAN MASYARAKAT', 'DAVID', 'FINN', 'CODE PINK', 'THE AMERICAN', 'AN AMERICAN', 'SUSHI RAN', 'BROWN', 'CONTRACTOR', 'MUSLIM', 'JOHN GALT', 'MEMBER', 'DOE', 'LOOSE CHANGE', 'SHERROD', 'JACKSON', 'TAU', 'COSA NOSTRA', 'PLANTANUS', 'TOURISTS', 'BUD LITE', 'BEHROOZ', 'WORKER', 'PEOPLE', 'LAL MASJID', 'BANK OF AMERICA CORP', 'MOM', 'ZAZZLE', 'SISTER', 'LA LINEA', 'HIM', 'LAMESTREAM', 'WEST', 'ZIRH', 'SAMBOL', 'MAILER', 'BROTHER', 'HERS', 'MOZILO', 'HELLO', 'CHRISTMAS', 'LATINO', 'SON', 'YISRAEL', 'HELEN KELLER INTERNATIONAL', 'DE IRAK', 'BETH SHALOM', 'KING', 'AMY GOODMAN', 'THELONEBEDOUIN', 'BERLINER BAROCK SOLISTEN', 'BUKIT ASAM', 'BEN HUR', 'BIOLOGIST', 'MAXFIELD', 'JERUSALEM , SEPT 14 , 2009', 'SKUD', 'DVDS', 'PASTOR', 'PACIFIC INVESTMENT MANAGEMENT CO', 'JDEVELOPER', 'HER', 'SIMILIE', 'LOWE', \"GANG `` GODMOTHER ''\", 'VAN WYCK', 'BANK OF AMERICA', 'PEARLMAN', 'DESILIENS', 'CHIEF', 'HE', 'A MAN', 'CHUNYUN', 'OSAMA-APPROVED AL-QUEADA', 'ABU SAYYAF', 'MUJAHID', 'GALLAGHER', 'BOY', 'YOUNG PILOT', 'AXELROD', 'HANUKKAH', 'LOVING MAILER', 'GENERAL RE', 'RUSSIAN MAN', 'ICONIC FIGHTER PILOT', 'CHILD', 'ROBERT DE NIRO', 'RUSSELL 2000', 'BUSH', 'CHEF', 'ROTHKO CHAPEL', 'JFK', 'HEPATITIS', 'RAND MCNALLY', 'THE LEADER OF THE GROUP OF AMERICANS', 'KULAYIGYE', 'A CHRISTIAN WOMAN', 'BLACK', 'SPVS', 'HIS FATHER', 'RICHARDSON', 'FRI', 'PILOT', 'BLYSKAL', 'CHRISTIAN WOMAN', 'THORP', 'GBAGBO', 'ITALY', 'NEW FABRIS', 'UNIVERSITY OF MARYLAND', 'MASSE', 'MINOW', '-RRB- --', 'FARIS JAWAD', 'COUNTRYWIDES', 'PATSY', 'MURDER', 'ROSS', 'PINTO', 'YOUNG PATRIOT', 'RICH MURPHY', 'HAHAHAHAHAHAHA', 'AIR EVAC EMS , INC', 'KMAQ', 'CATHOLIC', 'ALI', 'LAWYER', 'OUATTARA', 'BIN LADEN', '46', 'RICK MURPHY', 'ARTEMIDE', 'LATIN', 'LAWMAKER', 'MATHMETICIAN', 'GODMOTHER', 'ABU ALWAFA', 'HIS', 'NIELSEN']\n"
     ]
    }
   ],
   "source": [
    "subj_no_overlap_ls = list(set(range(len(test_examples))) - set(subj_overlap_ls))\n",
    "obj_no_overlap_ls = list(set(range(len(test_examples))) - set(obj_overlap_ls))\n",
    "\n",
    "no_named_organization_name_ls = [\n",
    "                test_examples[id_]['text'][\n",
    "                    test_examples[id_]['entity_spans'][0][0] \n",
    "                    : \n",
    "                    test_examples[id_]['entity_spans'][0][1]\n",
    "                    ].upper()\n",
    "                for id_ in subj_no_overlap_ls\n",
    "                if test_examples[id_]['entity_type'][0] == 'ORGANIZATION'\n",
    "                ] + \\\n",
    "                [\n",
    "                test_examples[id_]['text'][\n",
    "                    test_examples[id_]['entity_spans'][1][0] \n",
    "                    : \n",
    "                    test_examples[id_]['entity_spans'][1][1]\n",
    "                    ].upper()\n",
    "                for id_ in obj_no_overlap_ls\n",
    "                if test_examples[id_]['entity_type'][1] == 'ORGANIZATION'\n",
    "                ]\n",
    "\n",
    "no_named_person_name_ls = [\n",
    "                test_examples[id_]['text'][\n",
    "                    test_examples[id_]['entity_spans'][0][0] \n",
    "                    : \n",
    "                    test_examples[id_]['entity_spans'][0][1]\n",
    "                    ].upper()\n",
    "                for id_ in subj_no_overlap_ls\n",
    "                if test_examples[id_]['entity_type'][0] == 'PERSON'\n",
    "                ] + \\\n",
    "                [\n",
    "                test_examples[id_]['text'][\n",
    "                    test_examples[id_]['entity_spans'][1][0] \n",
    "                    : \n",
    "                    test_examples[id_]['entity_spans'][1][1]\n",
    "                    ].upper()\n",
    "                for id_ in obj_no_overlap_ls\n",
    "                if test_examples[id_]['entity_type'][1] == 'PERSON'\n",
    "                ]\n",
    "\n",
    "bad_org_name = ['ASSOCIATION', 'IT', 'HER', 'GROUP', 'SHE', 'THE FACTORY', 'EMAIL', 'HIS', 'COMPANY', 'ITS', 'HER', 'HIS', 'HOUSE', 'THE', 'HIM']\n",
    "good_per_name = ['SCRAPBLOG', 'PIEDRA', 'SYCAMORE', 'HELOISE', 'PIEDRA', 'ALESSI', 'AQUASH', 'CERNIGLIA', 'ANNA MAE', 'SEMEN GRESIK', 'ANDREW LANGE', 'MESSINA DENARO', 'RICHARD GAGE', \n",
    "                'NORRIS CHURCH MAILER', 'ASIA BIBI', 'FABRIS',  'NELL MINOW', 'PAUL HODGSON']\n",
    "bad_per_name = list(set(no_named_person_name_ls) - set(good_per_name))\n",
    "print('the bad person entities that cannot be replaced with other persons: ', bad_per_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cf46ab33",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4190, 5949, 7523, 7606, 7919, 8939, 10909, 12135, 13112]\n",
      "instanced id:  4190\n",
      "It traveled to Rice University , where de Menil and #his#(obj type:ORGANIZATION) wife , Dominique de Menil , who later founded the @Menil Collection@(subj type:ORGANIZATION) , ran the art museum .\n",
      "\n",
      "instanced id:  5949\n",
      "AJ - complete psychopath , mentally ill DRG - weasel and snake oil salesmen of the nth degree #Fetzer#(obj type:PERSON) - Loony toons SJ - ignorant of facts , and morally compromising for the sake of @his@(subj type:ORGANIZATION) argument .\n",
      "\n",
      "instanced id:  7523\n",
      "He figured that he would sell his home before the interest rate on the loan , taken out from @Countrywide Financial@(subj type:ORGANIZATION) , now owned by #Bank of America#(obj type:PERSON) , reset at a higher level .\n",
      "\n",
      "instanced id:  7606\n",
      "To him the walls of #his#(obj type:ORGANIZATION) prison are invisible , and @he@(subj type:ORGANIZATION) believes himself to be free .\n",
      "\n",
      "instanced id:  7919\n",
      "when Avery had to calm him down by touching @his@(subj type:ORGANIZATION) shoulder , which was quite possibly the most akward moment since #Rocky#(obj type:ORGANIZATION) 's and Apollo Creed 's man hug on a beach in Rocky III .\n",
      "\n",
      "instanced id:  8939\n",
      "`` When I was watching @Loose Change@(subj type:PERSON) with a friend of mine - he happens to work for a company that helps build the Boeing airplanes and they said that when the engines completely disappeared and were destroyed , #his#(obj type:ORGANIZATION) response was , excuse my French - bullshit !\n",
      "\n",
      "instanced id:  10909\n",
      "I was going to nominate Bobbie Balsamo , but in hindsight he has also been left in the dumpster , relegated to blatant milking of @his@(subj type:ORGANIZATION) flock through auto-renewing `` Chip In '' applets which scrape in enough money to keep #his#(obj type:ORGANIZATION) Bud Lite supply limping along .\n",
      "\n",
      "instanced id:  12135\n",
      "I know @Christopher Bollyn@(subj type:PERSON) , have talked to him on the phone and via e-mail exchanges , and I 've read most of #his#(obj type:ORGANIZATION) works , and I believe he is the same way .\n",
      "\n",
      "instanced id:  13112\n",
      "@His@(subj type:ORGANIZATION) followers come to my campus and talk about how awesome Kepler is and how #Obama#(obj type:PERSON) is a Nazi .\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bad_example_per_ls = ['BANK OF AMERICA']\n",
    "bad_example_org_ls = ['HIS']\n",
    "example_bad_id_ls = list(\n",
    "        [\n",
    "        id_\n",
    "        for id_ in range(len(test_examples))\n",
    "        if \n",
    "        (test_examples[id_]['entity_type'][0] == 'ORGANIZATION'\n",
    "        and test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][0][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][0][1]\n",
    "            ].upper() in bad_example_org_ls\n",
    "        )\n",
    "        or\n",
    "        (test_examples[id_]['entity_type'][0] == 'PERSON'\n",
    "        and test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][0][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][0][1]\n",
    "            ].upper() in bad_example_per_ls\n",
    "        )\n",
    "        or\n",
    "        (test_examples[id_]['entity_type'][1] == 'ORGANIZATION'\n",
    "        and test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][1][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][1][1]\n",
    "            ].upper() in bad_example_org_ls\n",
    "        )\n",
    "        or\n",
    "        (test_examples[id_]['entity_type'][1] == 'PERSON'\n",
    "        and test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][1][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][1][1]\n",
    "            ].upper() in bad_example_per_ls\n",
    "        )\n",
    "        ]\n",
    ")\n",
    "print(example_bad_id_ls)\n",
    "\n",
    "for id_ in example_bad_id_ls:\n",
    "    print('instanced id: ', id_)\n",
    "    printREInstance(test_examples[id_]['text'], \n",
    "                    test_examples[id_]['entity_spans'][0], test_examples[id_]['entity_spans'][1],\n",
    "                    test_examples[id_]['entity_type'][0], test_examples[id_]['entity_type'][1],\n",
    "                   )\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "162477c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of instances having subject for replacements:  10840\n",
      "number of instances having object for replacements:  5368\n"
     ]
    }
   ],
   "source": [
    "subj_final_id_ls = list(\n",
    "        [\n",
    "        id_\n",
    "        for id_ in range(len(test_examples))\n",
    "        if \n",
    "        (test_examples[id_]['entity_type'][0] == 'ORGANIZATION'\n",
    "        and test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][0][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][0][1]\n",
    "            ].upper() not in bad_org_name\n",
    "        )\n",
    "        or\n",
    "        (test_examples[id_]['entity_type'][0] == 'PERSON'\n",
    "        and test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][0][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][0][1]\n",
    "            ].upper() not in bad_per_name\n",
    "        )\n",
    "        ]\n",
    ")\n",
    "\n",
    "obj_final_id_ls = list(\n",
    "        [\n",
    "        id_\n",
    "        for id_ in range(len(test_examples))\n",
    "        if \n",
    "        (test_examples[id_]['entity_type'][1] == 'ORGANIZATION'\n",
    "        and test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][1][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][1][1]\n",
    "            ].upper() not in bad_org_name\n",
    "        )\n",
    "        or\n",
    "        (test_examples[id_]['entity_type'][1] == 'PERSON'\n",
    "        and test_examples[id_]['text'][\n",
    "            test_examples[id_]['entity_spans'][1][0] \n",
    "            : \n",
    "            test_examples[id_]['entity_spans'][1][1]\n",
    "            ].upper() not in bad_per_name\n",
    "        )\n",
    "        ]\n",
    ")\n",
    "\n",
    "print('number of instances having subject for replacements: ', len(subj_final_id_ls))\n",
    "print('number of instances having object for replacements: ', len(obj_final_id_ls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a48e91b0",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of instances having organization for replacements:  7681\n",
      "number of distinct organization names for replacements:  1308\n",
      "number of distinct organization names for replacements:  Counter({'SHOPPERTRAK': 271, 'CORPORATE LIBRARY': 247, 'ADF': 245, 'ALICO': 238, 'COUNTRYWIDE': 215, 'NATIONAL RESTAURANT ASSOCIATION': 212, 'PATA': 199, 'NATIONAL URBAN LEAGUE': 182, 'CHINA CHARITY FEDERATION': 167, 'NATIONAL DEVELOPMENT AND REFORM COMMISSION': 166, 'NDRC': 128, 'LIHOP': 124, 'PACIFIC ASIA TRAVEL ASSOCIATION': 122, 'NATIONAL CONGRESS OF AMERICAN INDIANS': 118, 'PROGRESSIVE DEMOCRATS OF AMERICA': 107, 'OANA': 106, 'TPI': 101, 'NATIONAL TAIWAN SYMPHONY ORCHESTRA': 99, 'NATIONAL ENERGY ADMINISTRATION': 94, 'AMERICAN FREE PRESS': 93, 'SYCAMORE': 87, 'UASR': 85, 'MENIL COLLECTION': 84, 'NDA': 81, 'NORRIS CHURCH': 79, 'PSIA': 76, 'NTSO': 75, 'PDA': 73, 'LOOKING CLOUD': 73, 'URBAN LEAGUE': 70, 'LOOSE CHANGE': 69, 'ARLO LOOKING CLOUD': 69, 'SEMEN GRESIK': 68, 'AFP': 56, 'KOCH FOODS': 43, 'AAUW': 43, 'COUNTRYWIDE FINANCIAL': 42, 'COUNTRYWIDE FINANCIAL CORP.': 38, 'NRA': 37, 'AIG': 37, 'NUCLEAR DECOMMISSIONING AUTHORITY': 36, 'TRIBECA ENTERPRISES': 34, 'MEF': 34, 'AMERICAN ASSOCIATION OF UNIVERSITY WOMEN': 33, 'NEW FABRIS': 30, 'NEA': 29, 'HKI': 29, 'UN': 28, 'CCF': 27, 'AUSTRALIAN DEFENCE FORCE': 26, 'TALIBAN': 26, 'METLIFE': 26, 'HELEN KELLER INTERNATIONAL': 25, 'NORRIS CHURCH MAILER': 25, 'HIGH POINT CHURCH': 23, 'ORGANIZATION OF ASIA-PACIFIC NEWS AGENCIES': 23, 'KOCH FOODS INC.': 23, 'AIA': 22, 'AUSTRALIAN DEFENSE FORCE': 21, 'MYANMAR EQUESTRIAN FEDERATION': 21, 'HELOISE': 20, 'MIHOP': 19, 'UNITED NATIONS': 18, 'CHONGQING MUNICIPAL JUDICIAL BUREAU': 18, 'COASTAL CONSERVATION LEAGUE': 17, 'SOCIAL IMPACT ANALYSIS': 17, 'DEFENCE FORCE': 17, 'PROFESSIONAL SKI INSTRUCTORS OF AMERICA': 16, 'NCAI': 16, 'AMERICAN LIFE INSURANCE COMPANY': 16, 'RED CROSS': 16, 'ORGANIZATION OF ASIA PACIFIC NEWS AGENCIES': 15, 'HE': 15, 'RED CROSS SOCIETY OF CHINA': 15, 'NAACP': 15, 'ORGANISATION OF ASIA-PACIFIC NEWS AGENCIES': 14, 'CENTRAL AMERICAN PARLIAMENT': 14, 'CONGRESS': 14, 'UNITED ASSOCIATION FOR STUDIES AND RESEARCH': 13, 'HOLLISTER INC.': 13, 'U.N.': 13, 'XINHUA': 13, 'THIRD GUARD DIVISION': 12, 'BANK OF AMERICA': 12, 'PLURIBUS CAPITAL MANAGEMENT': 11, 'SAP': 11, 'TAIWAN SYMPHONY ORCHESTRA': 11, 'GROSS': 11, 'LC': 10, 'ERP': 10, 'USING POVERTY AND SOCIAL IMPACT ANALYSIS': 10, 'DYLAN AVERY': 10, 'KBR': 10, 'A-ADOMESTIC LIFE': 10, 'SEC': 10, 'THE CHINA CHARITY FEDERATION': 9, 'THE MENIL COLLECTION': 9, 'ANADARKO': 9, 'CUSTOMS ENFORCEMENT': 9, 'ORACLE': 9, 'STATE DEPARTMENT': 9, 'HAMAS': 9, 'ORGANIZATION OF ASIA-PACIFIC NEWS AGENCY': 8, 'HUNTINGTON LIBRARY': 8, 'THAI PETROCHEMICAL INDUSTRY PLC': 8, 'UNITED SOLUTIONS AND SERVICES': 8, 'PIEDRA': 8, 'ACSE': 8, 'POVERTY AND SOCIAL ANALYSIS': 8, 'UNITED NEGRO COLLEGE FUND': 8, 'UNWTO': 8, 'MGE': 8, 'FBI': 8, 'HALLIBURTON': 8, 'AMERICAN LIFE INSURANCE CO.': 8, 'MINAS GERAIS': 8, 'BROTHERS': 8, 'CEPHALON': 8, 'TEMPAT PELELANGAN IKAN': 7, 'NO PLANERS': 7, 'COUNTRYWIDE FINANCIAL CORPORATION': 7, 'POVERTY AND SOCIAL IMPACT ANALYSIS OF REFORMS': 7, 'CIT': 7, 'BELLY OF THE GREEN BIRD': 7, 'SECURITY COUNCIL': 7, 'INTERNATIONAL COMMITTEE OF THE RED CROSS': 7, 'IRS': 7, 'AP': 7, 'LA LINEA': 7, 'THE NATIONAL RESTAURANT ASSOCIATION': 6, 'PACIFIC-ASIA TRAVEL ASSOCIATION': 6, 'COUNTRYWIDE FINANCIAL CORP': 6, 'QATALYS , INC': 6, 'MEDIO ORIENTE NIR ROSEN': 6, 'THE GOOD BROKER , LLC': 6, 'COUNCIL FOR CULTURAL AFFAIRS': 6, 'MCA': 6, 'GENERAL ASSEMBLY': 6, 'RCSC': 6, 'JUSTICE DEPARTMENT': 6, 'WORLD BANK': 6, 'STATE MEDIA': 6, 'PACKARD': 6, 'AMERICAN LIFE INSURANCE CO': 6, 'ARMY': 6, 'UNIVERSITY OF CALGARY': 6, 'YOUNG PATRIOTS': 6, 'FED': 6, 'AMERICAN INTERNATIONAL GROUP': 6, 'COUNTRYWIDE HOME LOANS': 5, \"GAY MEN 'S HEALTH CRISIS\": 5, 'PLURIBUS': 5, 'SYCAMORE SOFTWARE SOLUTIONS': 5, 'CFC': 5, 'PARLACEN': 5, 'ALLIED DEMOCRATIC FORCES': 5, 'TPIPL': 5, 'DEFENSE': 5, 'TRUTH': 5, 'POPULAR MECHANICS': 5, 'INTERNATIONAL COMMITTEE': 5, 'BANK OF AMERICA CORP.': 5, 'NYU': 5, 'YOUTUBE': 5, \"NEW LIFE CHILDREN 'S REFUGE\": 5, 'PARLIAMENT': 5, 'TIMES': 5, 'CHURCH OF SCIENTOLOGY': 5, 'ICRC': 5, 'PIA': 5, 'TRIUMPH OF THE MARTYRS': 5, 'NEW YORK TIMES': 5, 'MUSEUM OF MODERN ART': 5, 'BBC': 5, 'FAILURE OF NATION BUILDING': 5, 'WESTINGHOUSE': 5, \"INTERMEDIATE PEOPLE 'S COURT\": 5, 'UNIVERSITY OF WASHINGTON': 5, 'TRUTH MOVEMENT': 5, '`` YOUNG PATRIOTS': 5, 'MAFIA': 5, 'COUNTRYWIDE BANK': 4, 'ORGANIZATION OF ASIA - PACIFIC NEWS AGENCIES': 4, 'BEDBUG CENTRAL': 4, 'SCREW LOOSE CHANGE': 4, 'ASIA BIBI': 4, 'THE MENIL COLLECTION 1515 SUL ROSS': 4, 'DANCE THEATRE OF HARLEM': 4, 'TEXAS PETRO INDEX': 4, 'NUL': 4, 'POVERTY AND SOCIAL IMPACT ANALYSIS': 4, 'STATE DEVELOPMENT AND REFORM COMMISSION': 4, 'NEW AMERICA FOUNDATION': 4, 'SKY BLUE': 4, 'COSA NOSTRA': 4, 'ASSOCIATED PRESS': 4, 'FANNIE MAE': 4, 'CHONGQING': 4, 'AGENCY FOR INTERNATIONAL DEVELOPMENT': 4, 'UNIVERSITY OF CALIFORNIA': 4, 'EURO11': 4, 'EPF': 4, 'ANTARA': 4, 'NPM': 4, 'AQUASH': 4, 'CIVIL RIGHTS MOVEMENT FOR NATION MAGAZINE': 4, 'VETERANS FOR PEACE': 4, 'ABS-CBN': 4, 'PRINCETON UNIVERSITY': 4, 'U.S. CHAMBER OF COMMERCE': 4, 'BIG BANG': 4, 'THE ASSOCIATED PRESS': 4, 'TUSKEGEE': 4, 'MOSSAD': 4, 'FLAMENGO': 4, 'UNCF': 4, 'MINISTRY OF CIVIL AFFAIRS': 4, 'STARBUCKS': 4, 'PRINCETON': 4, 'JUSTICE BUREAU': 4, 'THAI PETROCHEMICAL INDUSTRY': 3, 'BESTPARTYEVERCOM': 3, 'SLC': 3, 'SYCAMORE VALLEY RANCH CO.': 3, 'HOLLISTER INC': 3, 'GREENBERG SMOKED TURKEY': 3, 'COUNTRYWIDE & # 8217': 3, 'THE NATIONAL ENERGY ADMINISTRATION': 3, 'MORITA CHEMICAL INDUSTRIES CO': 3, 'ASSOCIATION OF CHINESE-AMERICAN SCIENTISTS AND ENGINEERS': 3, 'HUNTINGTON LIBRARY , ART COLLECTIONS AND BOTANICAL GARDENS': 3, 'ANDOVER DEVELOPMENT FACILITY': 3, 'TRIBECA': 3, 'BUREAU OF INDIAN AFFAIRS': 3, 'GOP': 3, 'BEGINNING': 3, 'MOTOROLA': 3, 'PEUGEOT': 3, \"SOS CHILDREN 'S VILLAGES\": 3, 'E5': 3, 'LOS ANGELES NATIONAL IMPEACHMENT CENTER': 3, 'IRA': 3, 'NEW YORK FED': 3, 'RENAULT': 3, 'HOMELAND SECURITY': 3, 'NATIONAL RETAIL FEDERATION': 3, 'US AGENCY FOR INTERNATIONAL DEVELOPMENT': 3, 'CHONGQING MUNICIPALITY': 3, '`` DESCENT INTO CHAOS': 3, 'GUGGENHEIM PARTNERS': 3, 'NORAD': 3, 'PERUGIA': 3, 'INDOCEMENT': 3, 'WASHINGTON POST': 3, 'UNIVERSITY OF CHICAGO': 3, 'CCA': 3, 'AMERICAN INTERNATIONAL ASSURANCE': 3, 'CIA': 3, 'WAL-MART': 3, 'NEW YORK POST': 3, 'PUBLIC SECURITY BUREAU': 3, 'HOUSE OF REPRESENTATIVES': 3, 'TUFTS UNIVERSITY SCHOOL OF DENTAL MEDICINE': 3, 'IPC FOCUS': 3, 'ST. PETERSBURG TIMES': 3, 'AMERICAN LIFE INSURANCE': 3, 'NYSE': 3, 'MYANMAR BREWERY LTD': 3, 'DEVELOPMENT ALTERNATIVES INC': 3, 'WTTC': 3, \"ST. 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      "8527\n",
      "1231\n",
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MOZILO': 1, 'MARJORIE HILL': 1, 'MAL BROUGH': 1, 'ANDREW MORRIS': 1, 'CECALA': 1, 'GEORGE WILLEIT': 1, 'MARIE ANTOINETTE': 1, 'STEPHANIE J JONES': 1, 'ELIZABETH': 1, 'CRUSE': 1, 'JOSEPH NUSAIRAT': 1, 'TSAI CHIH-KUE': 1, 'DEANNA': 1, 'STEVE COBBLE': 1, 'MOUSA ABU MAZOOK': 1, 'SUNDER': 1, 'MICHAEL MOORES': 1, 'MARVIN BUSH': 1, 'MARK THOMAS': 1, 'PERRELLI': 1, 'FEDERICO LOMBARDI': 1, 'CAMNER': 1, 'MARK FISCHER': 1, 'FARRELL': 1, 'FITZGIBBON': 1, 'R. `` WILLIAM KUNSTLER': 1, 'WU GUIHUI': 1, 'RONALD ROSS': 1, 'BACON': 1, 'MA SHUI-LONG': 1, 'CRAIG HATKOFF': 1, 'PEARSON': 1, 'DALE LAWRENCE': 1, 'GREGORYULRICH': 1, 'MICHAEL BERNARD MUKASEY': 1, 'MICHIHIRO YASUDA': 1, 'FOXY KNOXY': 1, 'DIYA LUMUMBA': 1, 'ED `` LOU GRANT': 1, 'QIAN LONG': 1, 'BARB WONG': 1, 'ZHANG XIAOQIANG': 1, 'EZRA RANDLE': 1, 'GEORGE PACKER': 1, 'JOHN LEWIS': 1, 'MUKULU': 1, 'WHITECROSS': 1, 'MATTHEW DOULL': 1, 'JEAN-LOU CHAMEAU': 1, 'BARBARA HONEGGER': 1, 'WANG YIMING': 1, 'HENRY HUNTINGTON': 1, 'SAIF AL-ISLAM': 1, 'PONCE KIAH MARCHELLE HELOISE CRUSE EVANS': 1, 'KEVIN GREGG': 1, 'MOFFATT': 1, 'ARNON MILCHAN': 1, 'ANTARA AHMAD MUKHLIS YUSUF': 1, 'CONNOR': 1, 'TAGRO': 1, 'HAN YONGWEN': 1, 'MARION COTILLARD': 1, 'COLONEL ILAN RAMON': 1, 'JAMES MARTIN MURPHY': 1, 'CERMINARA': 1, 'GAYNELL DAVIS': 1, 'BOOBIE': 1, 'PANG CHENMIN': 1, 'JEREMY CORBYN': 1, 'HEIDI UEBERROTH': 1, 'DARNELL WILLIAMS': 1, 'MICK HUFF': 1, 'ROBIN HERMAN': 1, 'DAN': 1, 'MADDOX': 1, 'R.D.': 1, 'SUE LAFFERTY': 1, 'MEIGS': 1, 'WILKE': 1, 'STEPHEN SCHWARZMAN': 1, 'KOESTEY': 1, 'JERO WATJIK': 1, 'STEVEN C. ANDERSON': 1, 'CLAUDIE': 1, 'WILLIAM RODRIGUEZ': 1, 'CARLOS NOGUEIRA': 1, 'HEIKO MATHIAS FOERSTER': 1, 'JEREMY': 1, 'WINANS': 1, 'BRENDAN FLANAGAN': 1, 'BARBARA WASSERMAN': 1, 'LARA BURNS': 1, 'MARLENE DUMAS': 1, 'VAN TOL': 1, 'KONIN': 1, 'BENTLEY': 1, 'LAURENT KABILA': 1, 'PAUL SIMON': 1, 'EDWARD LIDDY': 1, 'BARRY MUKAMAL': 1, 'AZMAN UJANG': 1, 'TOM': 1, 'DAN A. BAILEY JR.': 1, 'DAVID PRICE': 1, 'HANS DOCKTER': 1, 'CROWLEY': 1, 'BAOJUN': 1, 'WILLIAM KNOX': 1, 'CHRIS MOYER': 1, 'MR SCHEUERMANN': 1, 'CARLA COOLMAN': 1, 'BAILEY': 1, 'DOTTIE': 1, 'MICHAEL C GIBBONS': 1, 'JOSEPH P. CONWAY': 1, 'LUCILLE BALL': 1, 'CAROLE MALLORY': 1, 'RONG HAO': 1, 'MARK ROBERTS': 1, 'CLINTON': 1, 'MUDD': 1, 'MARCOS APARECIDO SANTOS': 1, 'PHILIPPE STARCK': 1, 'FRANK BLAKE': 1, 'PENG': 1, 'LORNA LATHAM': 1, 'BOLLYN': 1, 'SHORT': 1, 'FILIPINA MARY JEAN LACABA': 1, 'HUGH': 1, 'PETER': 1, 'GERALD A. FERNANDEZ': 1, 'GEORGE H.W. BUSH': 1, 'LUKE': 1, 'JACKSON SLIM BROSSY': 1, 'CHARLA': 1, 'ROBERT PAPE': 1, 'JOHN PAUL II': 1, 'SYRON': 1, 'ROY WILKINS': 1, 'JULIE DARDANO': 1, 'LISA': 1, 'NIDIA DIAZ': 1, 'MICHAEL COLLINS PIPER': 1, 'BENNETT': 1, 'FESSLER': 1, 'OLIVER STONE': 1, 'JOHN BUFFALO': 1, 'PAUL HODGSEN': 1, 'SHELBY LAFRENIERE': 1, 'ALBERT': 1, 'GOBER': 1, 'CLEOPATRA': 1, 'SALMAAN TASEER': 1, 'WANG HUNING': 1, 'RON PAUL': 1, 'MIGNINI': 1, 'EUGENIO CLEMEN': 1, 'NIXON': 1, 'JUERGEN HOELLER': 1, 'STEWIE': 1})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "final_organization_name_ls = [\n",
    "                test_examples[id_]['text'][\n",
    "                    test_examples[id_]['entity_spans'][0][0] \n",
    "                    : \n",
    "                    test_examples[id_]['entity_spans'][0][1]\n",
    "                    ].upper()\n",
    "                for id_ in subj_final_id_ls\n",
    "                if test_examples[id_]['entity_type'][0] == 'ORGANIZATION'\n",
    "                ] + \\\n",
    "                [\n",
    "                test_examples[id_]['text'][\n",
    "                    test_examples[id_]['entity_spans'][1][0] \n",
    "                    : \n",
    "                    test_examples[id_]['entity_spans'][1][1]\n",
    "                    ].upper()\n",
    "                for id_ in obj_final_id_ls\n",
    "                if test_examples[id_]['entity_type'][1] == 'ORGANIZATION'\n",
    "                ]\n",
    "\n",
    "print('number of instances having organization for replacements: ', len(final_organization_name_ls))\n",
    "print('number of distinct organization names for replacements: ', len(set(final_organization_name_ls)))\n",
    "print('number of distinct organization names for replacements: ', Counter(final_organization_name_ls))\n",
    "\n",
    "final_person_name_ls = [\n",
    "                test_examples[id_]['text'][\n",
    "                    test_examples[id_]['entity_spans'][0][0] \n",
    "                    : \n",
    "                    test_examples[id_]['entity_spans'][0][1]\n",
    "                    ].upper()\n",
    "                for id_ in subj_final_id_ls\n",
    "                if test_examples[id_]['entity_type'][0] == 'PERSON'\n",
    "                ] + \\\n",
    "                [\n",
    "                test_examples[id_]['text'][\n",
    "                    test_examples[id_]['entity_spans'][1][0] \n",
    "                    : \n",
    "                    test_examples[id_]['entity_spans'][1][1]\n",
    "                    ].upper()\n",
    "                for id_ in obj_final_id_ls\n",
    "                if test_examples[id_]['entity_type'][1] == 'PERSON'\n",
    "                ]\n",
    "\n",
    "print(len(final_person_name_ls))\n",
    "print(len(set(final_person_name_ls)))\n",
    "print(Counter(final_person_name_ls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ca2b151d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of instances for replacements:  12193\n",
      "number of distinct relations for replacements:  42\n",
      "counter of relation labels for replacements:  Counter({'no_relation': 9247, 'per:title': 410, 'org:top_members/employees': 346, 'per:employee_of': 264, 'org:alternate_names': 213, 'per:age': 169, 'per:cities_of_residence': 129, 'per:origin': 114, 'org:country_of_headquarters': 107, 'per:countries_of_residence': 104, 'per:parents': 83, 'org:city_of_headquarters': 82, 'per:charges': 80, 'org:founded_by': 68, 'org:parents': 62, 'per:spouse': 62, 'per:stateorprovinces_of_residence': 57, 'per:siblings': 51, 'per:other_family': 51, 'org:stateorprovince_of_headquarters': 51, 'org:subsidiaries': 44, 'per:cause_of_death': 40, 'per:date_of_death': 39, 'org:founded': 36, 'per:children': 32, 'org:members': 31, 'per:religion': 31, 'per:schools_attended': 30, 'org:website': 24, 'per:city_of_death': 23, 'org:member_of': 18, 'org:number_of_employees/members': 17, 'org:shareholders': 13, 'per:alternate_names': 11, 'per:stateorprovince_of_death': 11, 'org:political/religious_affiliation': 9, 'per:date_of_birth': 8, 'per:country_of_death': 8, 'per:stateorprovince_of_birth': 6, 'per:country_of_birth': 5, 'per:city_of_birth': 5, 'org:dissolved': 2})\n",
      "\n",
      "classes of small number of instances:  ['per:country_of_death', 'org:number_of_employees/members', 'per:alternate_names', 'per:date_of_birth', 'per:stateorprovince_of_birth', 'per:city_of_birth', 'org:political/religious_affiliation', 'org:dissolved', 'org:member_of', 'org:shareholders', 'per:country_of_birth', 'per:stateorprovince_of_death']\n"
     ]
    }
   ],
   "source": [
    "final_id_ls = list(set(subj_final_id_ls + obj_final_id_ls))\n",
    "\n",
    "final_relation_ls = [\n",
    "        test_examples[id_]['label']\n",
    "        for id_ in list(set(subj_final_id_ls + obj_final_id_ls))\n",
    "        ]\n",
    "\n",
    "print('number of instances for replacements: ', len(final_relation_ls))\n",
    "print('number of distinct relations for replacements: ', len(set(final_relation_ls)))\n",
    "\n",
    "label_counter = Counter(final_relation_ls)\n",
    "print('counter of relation labels for replacements: ', label_counter)\n",
    "print()\n",
    "\n",
    "minor_class = [\n",
    "    class_\n",
    "    for class_ in list(set(final_relation_ls))\n",
    "    if label_counter[class_] < 20\n",
    "]\n",
    "print('classes of small number of instances: ', minor_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c577c81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['final_id_resample_ls.output']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_id_resample_ls = final_id_ls + [i_ for i_ in final_id_ls if test_examples[i_]['label'] in minor_class] * 2\n",
    "joblib.dump((final_id_resample_ls, subj_final_id_ls, obj_final_id_ls), 'final_id_resample_ls.output')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6784bf74-4167-4343-b577-7b000a7e4714",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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