dannlp_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DaNLP Test\n",
    "\n",
    "Test the DaNLP software for linguistic processing of Danish text, in particular the dependency parsing. Links:\n",
    "    \n",
    "* Installation: https://pypi.org/project/danlp/\n",
    "* Dependency parsing: https://github.com/alexandrainst/danlp/blob/master/docs/docs/tasks/dependency.md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import danlp\n",
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "from danlp.models import load_spacy_model\n",
    "from danlp.models import load_bert_ner_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Syntactic analysis with DaNLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 POS tagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = load_spacy_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Jeg</td>\n",
       "      <td>PRON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>hopper</td>\n",
       "      <td>VERB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>på</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>en</td>\n",
       "      <td>DET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>bil</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>,</td>\n",
       "      <td>PUNCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>som</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>er</td>\n",
       "      <td>AUX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>rød</td>\n",
       "      <td>ADJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>sammen</td>\n",
       "      <td>ADV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>med</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Niels</td>\n",
       "      <td>PROPN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>.</td>\n",
       "      <td>PUNCT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      text    pos\n",
       "0      Jeg   PRON\n",
       "1   hopper   VERB\n",
       "2       på    ADP\n",
       "3       en    DET\n",
       "4      bil   NOUN\n",
       "5        ,  PUNCT\n",
       "6      som    ADP\n",
       "7       er    AUX\n",
       "8      rød    ADJ\n",
       "9   sammen    ADV\n",
       "10     med    ADP\n",
       "11   Niels  PROPN\n",
       "12       .  PUNCT"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://github.com/alexandrainst/danlp/blob/master/docs/docs/tasks/pos.md\n",
    "\n",
    "doc = nlp('Jeg hopper på en bil, som er rød sammen med Niels.')\n",
    "analysis = []\n",
    "for token in doc:\n",
    "    analysis.append({\"text\": token.text, \"pos\": token.pos_})\n",
    "pd.DataFrame(analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Dependency parsing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(doc):\n",
    "    analysis = []\n",
    "    for token in doc:\n",
    "        analysis.append({ \"text\": token.text, \"lemma\": token.lemma_, \"head\": token.head.i, \"dep\": token.dep_, \"pos\": token.pos_ })\n",
    "    return pd.DataFrame(analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>lemma</th>\n",
       "      <th>head</th>\n",
       "      <th>dep</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Ordene</td>\n",
       "      <td>Ordene</td>\n",
       "      <td>1</td>\n",
       "      <td>nsubj</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>sættes</td>\n",
       "      <td>sættes</td>\n",
       "      <td>1</td>\n",
       "      <td>ROOT</td>\n",
       "      <td>VERB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>sammen</td>\n",
       "      <td>sammen</td>\n",
       "      <td>1</td>\n",
       "      <td>advmod</td>\n",
       "      <td>ADV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>til</td>\n",
       "      <td>til</td>\n",
       "      <td>5</td>\n",
       "      <td>case</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>meningsfulde</td>\n",
       "      <td>meningsfulde</td>\n",
       "      <td>5</td>\n",
       "      <td>amod</td>\n",
       "      <td>ADJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>sætninger</td>\n",
       "      <td>sætninger</td>\n",
       "      <td>2</td>\n",
       "      <td>obl</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>1</td>\n",
       "      <td>punct</td>\n",
       "      <td>PUNCT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           text         lemma  head     dep    pos\n",
       "0        Ordene        Ordene     1   nsubj   NOUN\n",
       "1        sættes        sættes     1    ROOT   VERB\n",
       "2        sammen        sammen     1  advmod    ADV\n",
       "3           til           til     5    case    ADP\n",
       "4  meningsfulde  meningsfulde     5    amod    ADJ\n",
       "5     sætninger     sætninger     2     obl   NOUN\n",
       "6             .             .     1   punct  PUNCT"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://github.com/alexandrainst/danlp/blob/master/docs/docs/tasks/dependency.md\n",
    "\n",
    "doc = nlp('Ordene sættes sammen til meningsfulde sætninger.')\n",
    "print_results(doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Named Entity Recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading file /tmp/tmpu9z17901\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% |#########################################################################|\n"
     ]
    }
   ],
   "source": [
    "bert = load_bert_ner_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': 'Som Paul W. Franks , professor i genetisk epidemiologi ved Lund Universitet , bemærker',\n",
       " 'entities': [{'type': 'PER',\n",
       "   'text': 'Paul W. Franks',\n",
       "   'start_pos': 4,\n",
       "   'end_pos': 18},\n",
       "  {'type': 'ORG', 'text': 'Lund Universitet', 'start_pos': 59, 'end_pos': 75}]}"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bert.predict([ \"Som\", \"Paul\", \"W.\", \"Franks\", \",\", \"professor\", \"i\", \"genetisk\", \"epidemiologi\", \"ved\", \"Lund\", \"Universitet\", \",\", \"bemærker\" ], IOBformat=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Process texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_texts(directory):\n",
    "    file_names = os.listdir(directory)\n",
    "    texts = {}\n",
    "    for file_name in file_names:\n",
    "        if re.search(\"\\.txt$\", file_name):\n",
    "            texts[file_name] = \"\"\n",
    "            infile = open(f\"{directory}/{file_name}\", \"r\")\n",
    "            for line in infile:\n",
    "                texts[file_name] += line\n",
    "            infile.close()\n",
    "    return texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_ner_analysis(text_tokens):\n",
    "    paragraph_tokens = []\n",
    "    ner_analysis = []\n",
    "    for token in text_tokens:\n",
    "        if token in [ '\\n\\n', '\\n', '.', ' ' ]:\n",
    "            if len(paragraph_tokens) > 0:\n",
    "                ner_analysis.append(bert.predict(paragraph_tokens, IOBformat=False))\n",
    "            paragraph_tokens = []\n",
    "        else:\n",
    "            paragraph_tokens.append(token)\n",
    "    if len(paragraph_tokens) > 0:\n",
    "        ner_analysis.append(bert.predict(paragraph_tokens, IOBformat=False))\n",
    "    return ner_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "texts = read_texts(\"../danish\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There three texts: \"20200528.txt\" (longest), \"20201213.txt\" (shortest) and \"20211212.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>lemma</th>\n",
       "      <th>head</th>\n",
       "      <th>dep</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Oprindeligt</td>\n",
       "      <td>Oprindeligt</td>\n",
       "      <td>18</td>\n",
       "      <td>flat</td>\n",
       "      <td>ADV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>udgivet</td>\n",
       "      <td>udgivet</td>\n",
       "      <td>6</td>\n",
       "      <td>acl:relcl</td>\n",
       "      <td>VERB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>af</td>\n",
       "      <td>af</td>\n",
       "      <td>28</td>\n",
       "      <td>case</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Gatestone</td>\n",
       "      <td>Gatestone</td>\n",
       "      <td>26</td>\n",
       "      <td>obl</td>\n",
       "      <td>PROPN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Institute</td>\n",
       "      <td>Institute</td>\n",
       "      <td>28</td>\n",
       "      <td>flat</td>\n",
       "      <td>PROPN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>3</td>\n",
       "      <td>punct</td>\n",
       "      <td>PUNCT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           text        lemma  head        dep    pos\n",
       "25  Oprindeligt  Oprindeligt    18       flat    ADV\n",
       "26      udgivet      udgivet     6  acl:relcl   VERB\n",
       "27           af           af    28       case    ADP\n",
       "28    Gatestone    Gatestone    26        obl  PROPN\n",
       "29    Institute    Institute    28       flat  PROPN\n",
       "30            .            .     3      punct  PUNCT"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc = nlp(texts[\"20200528.txt\"])\n",
    "nlp_analysis = print_results(doc)\n",
    "nlp_analysis[25:31]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'text': 'Den svenske “ model ” til bekæmpelse af coronavirus',\n",
       "  'entities': []},\n",
       " {'text': '28. maj 2020', 'entities': []},\n",
       " {'text': '24 NYT Sverige , Tophistorie',\n",
       "  'entities': [{'type': 'LOC',\n",
       "    'text': 'Sverige',\n",
       "    'start_pos': 7,\n",
       "    'end_pos': 14}]}]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ner_analysis = make_ner_analysis([ token.text for token in doc ])\n",
    "ner_analysis[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Who, where, when, what, why and how?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_results(results_dict):\n",
    "    results_df = pd.DataFrame([{ \"key\": key, \"count\": results_dict[key], \"best value\": \"\"} \n",
    "                               for key in sorted(results_dict, key=lambda k: results_dict[k], reverse=True)])\n",
    "    best_keys = get_best_keys(results_df)\n",
    "    for i, row in results_df.iterrows():\n",
    "        if row[\"key\"] in best_keys:\n",
    "            results_df.at[i, \"best value\"] = \"yes\"\n",
    "    return results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_best_keys(results_df):\n",
    "    best_count = -1\n",
    "    best_keys = []\n",
    "    for i, row in results_df.iterrows():\n",
    "        if row[\"count\"] > best_count:\n",
    "            best_count = row[\"count\"]\n",
    "            best_keys = [row[\"key\"]]\n",
    "        elif row[\"count\"] == best_count:\n",
    "            best_keys.append(row[\"key\"])\n",
    "    case_is_upper = []\n",
    "    for key in best_keys:\n",
    "        case_is_upper.append(re.search(r\"^[A-Z]\", key) != None)\n",
    "    if True in case_is_upper:\n",
    "        best_keys = [ best_keys[i] for i in range(0, len(best_keys)) if case_is_upper[i] ] \n",
    "    return best_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_actions(nlp_table_df):\n",
    "    actions = {}\n",
    "    for i, row in nlp_table_df.iterrows():\n",
    "        if row[\"pos\"] == \"VERB\" and re.search(\"[a-zA-Z]\", row[\"text\"]):\n",
    "            if row[\"text\"] in actions:\n",
    "                actions[row[\"text\"]] += 1\n",
    "            else:\n",
    "                actions[row[\"text\"]] = 1\n",
    "    return actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_actors(nlp_table_df):\n",
    "    actors = {}\n",
    "    for i, row in nlp_table_df.iterrows():\n",
    "        if row[\"dep\"] == \"nsubj\":\n",
    "            actor = row[\"text\"]\n",
    "        else:\n",
    "            actor = \"\"\n",
    "        if actor != \"\":\n",
    "            if actor in actors:\n",
    "                actors[actor] += 1\n",
    "            else:\n",
    "                actors[actor] = 1\n",
    "    return actors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_locations(ner_analysis):\n",
    "    locations = {}\n",
    "    for paragraph in ner_analysis:\n",
    "        for entity in paragraph[\"entities\"]:\n",
    "            if entity[\"type\"] == \"LOC\":\n",
    "                location = entity[\"text\"]\n",
    "            else:\n",
    "                location = \"\"\n",
    "            if location != \"\":\n",
    "                if location in locations:\n",
    "                    locations[location] += 1\n",
    "                else:\n",
    "                    locations[location] = 1\n",
    "    return locations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing: times, causes, manners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>count</th>\n",
       "      <th>best value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>er</td>\n",
       "      <td>10</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>har</td>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>sagde</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>mener</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>have</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     key  count best value\n",
       "0     er     10        yes\n",
       "1    har      5           \n",
       "2  sagde      4           \n",
       "3  mener      3           \n",
       "4   have      3           "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_results(get_actions(nlp_analysis))[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Sverige</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>1</td>\n",
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       "      <td></td>\n",
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       "      <td>2</td>\n",
       "      <td>vi</td>\n",
       "      <td>9</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>de</td>\n",
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       "      <td></td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>man</td>\n",
       "      <td>8</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Tegnell</td>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>det</td>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>han</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>WHO</td>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Jeg</td>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       key  count best value\n",
       "0  Sverige     13        yes\n",
       "1      der     10           \n",
       "2       vi      9           \n",
       "3       de      8           \n",
       "4      man      8           \n",
       "5  Tegnell      6           \n",
       "6      det      6           \n",
       "7      han      4           \n",
       "8      WHO      2           \n",
       "9      Jeg      2           "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_results(get_actors(nlp_analysis))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>Sverige</td>\n",
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       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Iran</td>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Sveriges</td>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Danmark</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Norge</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        key  count best value\n",
       "0   Sverige     21        yes\n",
       "1      Iran      5           \n",
       "2  Sveriges      5           \n",
       "3   Danmark      3           \n",
       "4     Norge      3           "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_results(get_locations(ner_analysis))[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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