diff --git "a/openwordnet_to_categoricals.ipynb" "b/openwordnet_to_categoricals.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2FzBzmpBRkV3"
+ },
+ "source": [
+ "# Checking Embeddings of Terms (Noun/Verb/Adj/etc.) from Tagged Wordnet Gloss\n",
+ "\n",
+ "I discovered there's a more active fork of wordnet and bumped this analysis over to that."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install datasets"
+ ],
+ "metadata": {
+ "id": "K5C1kaWhXnJf",
+ "outputId": "5b4045f0-9aa2-4579-d52c-4f45e1d67180",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.18.0)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.13.1)\n",
+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n",
+ "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (14.0.2)\n",
+ "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n",
+ "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
+ "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
+ "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.2)\n",
+ "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n",
+ "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n",
+ "Requirement already satisfied: fsspec[http]<=2024.2.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
+ "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.3)\n",
+ "Requirement already satisfied: huggingface-hub>=0.19.4 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.20.3)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.0)\n",
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n",
+ "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
+ "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.4->datasets) (4.10.0)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.3.2)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.6)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.7)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2024.2.2)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "# Load the dataset\n",
+ "dataset = load_dataset(\"jon-tow/open-english-wordnet-synset-2023\")"
+ ],
+ "metadata": {
+ "id": "n12stD5MRnek"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "_D-Y5nf6RkV4",
+ "outputId": "a205d054-7fab-477d-eddb-9be56942891c",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'@id': 'oewn-03159292-a',\n",
+ " '@ili': 'i18097',\n",
+ " '@members': 'oewn-avenged-a',\n",
+ " '@partOfSpeech': 'a',\n",
+ " '@lexfile': 'adj.ppl',\n",
+ " 'Definition': 'for which vengeance has been taken',\n",
+ " 'SynsetRelation': [],\n",
+ " 'Example': 'an avenged injury',\n",
+ " 'ILIDefinition': None,\n",
+ " '@dc:source': None}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 40
+ }
+ ],
+ "source": [
+ "dataset['train'][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import pandas as pd"
+ ],
+ "metadata": {
+ "id": "ioCtYnx7gDo6"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df = pd.DataFrame(dataset['train'])"
+ ],
+ "metadata": {
+ "id": "g6voyMIugE4c"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df.head()"
+ ],
+ "metadata": {
+ "id": "WNmdjublgIXz",
+ "outputId": "90ff3c7f-7ac6-4f59-df79-c96b6a5f75ad",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " @id @ili @members @partOfSpeech \\\n",
+ "0 oewn-03159292-a i18097 oewn-avenged-a a \n",
+ "1 oewn-03159419-a i18098 oewn-unavenged-a a \n",
+ "2 oewn-03159554-a i18099 oewn-beaten-a a \n",
+ "3 oewn-03159654-a i18100 oewn-calibrated-a oewn-graduated-a a \n",
+ "4 oewn-03159804-a i18101 oewn-cantering-a a \n",
+ "\n",
+ " @lexfile Definition SynsetRelation \\\n",
+ "0 adj.ppl for which vengeance has been taken [] \n",
+ "1 adj.ppl for which vengeance has not been taken [] \n",
+ "2 adj.ppl formed or made thin by hammering [] \n",
+ "3 adj.ppl marked with or divided into degrees [] \n",
+ "4 adj.ppl riding at a gait between a trot and a gallop [] \n",
+ "\n",
+ " Example ILIDefinition @dc:source \n",
+ "0 an avenged injury None None \n",
+ "1 an unavenged murder None None \n",
+ "2 beaten gold None None \n",
+ "3 a calibrated thermometer None None \n",
+ "4 the cantering soldiers None None "
+ ],
+ "text/html": [
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+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "df"
+ }
+ },
+ "metadata": {},
+ "execution_count": 43
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Getting the @members into a reasonable format is about to take a bunch of cells and most of my patience for the day."
+ ],
+ "metadata": {
+ "id": "vJM-9DJE1Oaq"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df.shape"
+ ],
+ "metadata": {
+ "id": "L9zrnh6Urqco",
+ "outputId": "1ace16a7-578a-4fa3-b474-5379d2a10248",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(120135, 10)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 44
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df[['@members', '@partOfSpeech', '@lexfile']].head()"
+ ],
+ "metadata": {
+ "id": "z8c4VmJ6lYwa",
+ "outputId": "c0446594-dfcb-4681-95c9-0028efa128a1",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " @members @partOfSpeech @lexfile\n",
+ "0 oewn-avenged-a a adj.ppl\n",
+ "1 oewn-unavenged-a a adj.ppl\n",
+ "2 oewn-beaten-a a adj.ppl\n",
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+ "4 oewn-cantering-a a adj.ppl"
+ ],
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+ "\n",
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+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "summary": "{\n \"name\": \"df[['@members', '@partOfSpeech', '@lexfile']]\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"@members\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"oewn-unavenged-a\",\n \"oewn-cantering-a\",\n \"oewn-beaten-a\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"@partOfSpeech\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"a\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"@lexfile\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"adj.ppl\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
+ }
+ },
+ "metadata": {},
+ "execution_count": 45
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df = df[['@members', '@partOfSpeech', '@lexfile']]"
+ ],
+ "metadata": {
+ "id": "bobEK-ZsllHr"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "pattern = r'^(\\w+-\\w+-\\w+ *)*$'\n",
+ "\n",
+ "matches_pattern = df['@members'].str.match(pattern)\n",
+ "\n",
+ "all_match_pattern = matches_pattern.all()\n",
+ "all_match_pattern"
+ ],
+ "metadata": {
+ "id": "LqERc6pcFyao",
+ "outputId": "1fd3bad6-f639-49cf-ca84-a23003616511",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 47
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "members_not_matching_pattern = df[~matches_pattern]\n",
+ "members_not_matching_pattern"
+ ],
+ "metadata": {
+ "id": "jLvqtPRvGeqN",
+ "outputId": "25982f3b-050e-427d-c953-e91fc7e1ed35",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 423
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
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+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "members_not_matching_pattern",
+ "summary": "{\n \"name\": \"members_not_matching_pattern\",\n \"rows\": 6987,\n \"fields\": [\n {\n \"column\": \"@members\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6837,\n \"samples\": [\n \"oewn-ichthyolatry-n oewn-fish-worship-n\",\n \"oewn-record-breaker-n oewn-record-holder-n\",\n \"oewn-green-white-a oewn-greenish-white-a\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"@partOfSpeech\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"n\",\n \"r\",\n \"s\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"@lexfile\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 43,\n \"samples\": [\n \"noun.possession\",\n \"noun.substance\",\n \"noun.location\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
+ }
+ },
+ "metadata": {},
+ "execution_count": 48
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "pattern = r'^(\\w+-[\\w_\\-.]+-\\w+ *)*$' # This took a couple of iterations not represented\n",
+ "\n",
+ "matches_pattern = df['@members'].str.match(pattern)\n",
+ "\n",
+ "all_match_pattern = matches_pattern.all()\n",
+ "all_match_pattern"
+ ],
+ "metadata": {
+ "id": "m8Jo9CRfOANO",
+ "outputId": "ea4f54f7-1d25-4fc3-9ba8-70ae5000dae1",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 49
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# I found this problem later on\n",
+ "oddball = df['@members'].str.match('.*Gravenhage.*')\n",
+ "oddball_member = df[oddball]['@members']"
+ ],
+ "metadata": {
+ "id": "jsFdSy6qmhmG"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "oddball_member.iloc[0]"
+ ],
+ "metadata": {
+ "id": "AzVbuHXGnSkK",
+ "outputId": "ca09912d-6b62-405e-b4bb-54eb62d4ab70",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'oewn-The_Hague-n oewn--ap-s_Gravenhage-n oewn-Den_Haag-n'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 51
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df = df.assign(members=df['@members'].str.split()).explode('members')"
+ ],
+ "metadata": {
+ "id": "J9uuPTGBqeVe"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df.head()"
+ ],
+ "metadata": {
+ "id": "rNbH6RL4rRPG",
+ "outputId": "b37b227f-bfe7-4420-af53-613d1ddc204e",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " @members @partOfSpeech @lexfile \\\n",
+ "0 oewn-avenged-a a adj.ppl \n",
+ "1 oewn-unavenged-a a adj.ppl \n",
+ "2 oewn-beaten-a a adj.ppl \n",
+ "3 oewn-calibrated-a oewn-graduated-a a adj.ppl \n",
+ "3 oewn-calibrated-a oewn-graduated-a a adj.ppl \n",
+ "\n",
+ " members \n",
+ "0 oewn-avenged-a \n",
+ "1 oewn-unavenged-a \n",
+ "2 oewn-beaten-a \n",
+ "3 oewn-calibrated-a \n",
+ "3 oewn-graduated-a "
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ "
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+ "\n",
+ "
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+ " \n",
+ " \n",
+ " | \n",
+ " @members | \n",
+ " @partOfSpeech | \n",
+ " @lexfile | \n",
+ " members | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " oewn-avenged-a | \n",
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+ "
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+ " \n",
+ " 1 | \n",
+ " oewn-unavenged-a | \n",
+ " a | \n",
+ " adj.ppl | \n",
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+ "
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+ " \n",
+ " 2 | \n",
+ " oewn-beaten-a | \n",
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+ " adj.ppl | \n",
+ " oewn-beaten-a | \n",
+ "
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+ " \n",
+ " 3 | \n",
+ " oewn-calibrated-a oewn-graduated-a | \n",
+ " a | \n",
+ " adj.ppl | \n",
+ " oewn-calibrated-a | \n",
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+ " \n",
+ " 3 | \n",
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+ " adj.ppl | \n",
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+ "
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+ " \n",
+ "
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+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "df"
+ }
+ },
+ "metadata": {},
+ "execution_count": 53
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df.shape"
+ ],
+ "metadata": {
+ "id": "TTdfz_HkrT4r",
+ "outputId": "35d0ad6f-1b81-4eb6-988d-effd68e5e4b4",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(212071, 4)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 54
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "prefixes = df['members'].str.split('-', expand=True)[0]\n",
+ "prefix_freq = prefixes.value_counts().reset_index()\n",
+ "prefix_freq.columns = ['Prefix', 'Frequency']\n",
+ "\n",
+ "prefix_freq = prefix_freq.sort_values(by='Frequency', ascending=False)\n",
+ "\n",
+ "print(prefix_freq)"
+ ],
+ "metadata": {
+ "id": "A6zydv3gq4IZ",
+ "outputId": "ea147541-f0c2-4dcc-f51c-1060507ab527",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " Prefix Frequency\n",
+ "0 oewn 212071\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "to_remove = 'oewn-'\n",
+ "\n",
+ "df['members'] = df['members'].apply(lambda x: x.replace(to_remove, '') if x.startswith(to_remove) else x)"
+ ],
+ "metadata": {
+ "id": "orcYSJC-rL_d"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "prefixes = df['members'].str.split('-', expand=True)[0]\n",
+ "prefix_freq = prefixes.value_counts().reset_index()\n",
+ "prefix_freq.columns = ['Prefix', 'Frequency']\n",
+ "\n",
+ "prefix_freq = prefix_freq.sort_values(by='Frequency', ascending=False)\n",
+ "\n",
+ "print(prefix_freq)"
+ ],
+ "metadata": {
+ "id": "Zne276Jardwg",
+ "outputId": "b0d9aacb-2504-4e66-96cb-f8b8d4d72ea3",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " Prefix Frequency\n",
+ "0 self 252\n",
+ "1 high 102\n",
+ "2 well 98\n",
+ "3 one 85\n",
+ "4 cut 79\n",
+ "... ... ...\n",
+ "66494 CIA 1\n",
+ "66493 National_Institute_of_Standards_and_Technology 1\n",
+ "66492 Counterterrorist_Center 1\n",
+ "66491 Nonproliferation_Center 1\n",
+ "145809 grammatical_cohesion 1\n",
+ "\n",
+ "[145810 rows x 2 columns]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Check for values starting with \"-\"\n",
+ "values_starting_with_dash = df[df['members'].str.startswith('-')]\n",
+ "\n",
+ "# Display the values starting with \"-\"\n",
+ "print(values_starting_with_dash)"
+ ],
+ "metadata": {
+ "id": "sk2wdpTRsKhT",
+ "outputId": "e058e104-1b4d-463e-e046-8a550f8984c6",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " @members @partOfSpeech \\\n",
+ "81633 oewn-The_Hague-n oewn--ap-s_Gravenhage-n oewn-... n \n",
+ "106115 oewn-between-r oewn--ap-tween-r r \n",
+ "107858 oewn-between_decks-r oewn--ap-tween_decks-r r \n",
+ "114349 oewn-hood-n oewn--ap-hood-n n \n",
+ "\n",
+ " @lexfile members \n",
+ "81633 noun.location -ap-s_Gravenhage-n \n",
+ "106115 adv.all -ap-tween-r \n",
+ "107858 adv.all -ap-tween_decks-r \n",
+ "114349 noun.group -ap-hood-n \n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df.head()"
+ ],
+ "metadata": {
+ "id": "I1ihnJ0RrmHy",
+ "outputId": "f3047e65-2191-4286-fd7d-1502aaab8842",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " @members @partOfSpeech @lexfile members\n",
+ "0 oewn-avenged-a a adj.ppl avenged-a\n",
+ "1 oewn-unavenged-a a adj.ppl unavenged-a\n",
+ "2 oewn-beaten-a a adj.ppl beaten-a\n",
+ "3 oewn-calibrated-a oewn-graduated-a a adj.ppl calibrated-a\n",
+ "3 oewn-calibrated-a oewn-graduated-a a adj.ppl graduated-a"
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ "
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+ "\n",
+ "
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+ " \n",
+ " \n",
+ " | \n",
+ " @members | \n",
+ " @partOfSpeech | \n",
+ " @lexfile | \n",
+ " members | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " oewn-avenged-a | \n",
+ " a | \n",
+ " adj.ppl | \n",
+ " avenged-a | \n",
+ "
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+ " \n",
+ " 1 | \n",
+ " oewn-unavenged-a | \n",
+ " a | \n",
+ " adj.ppl | \n",
+ " unavenged-a | \n",
+ "
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+ " \n",
+ " 2 | \n",
+ " oewn-beaten-a | \n",
+ " a | \n",
+ " adj.ppl | \n",
+ " beaten-a | \n",
+ "
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+ " \n",
+ " 3 | \n",
+ " oewn-calibrated-a oewn-graduated-a | \n",
+ " a | \n",
+ " adj.ppl | \n",
+ " calibrated-a | \n",
+ "
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+ " \n",
+ " 3 | \n",
+ " oewn-calibrated-a oewn-graduated-a | \n",
+ " a | \n",
+ " adj.ppl | \n",
+ " graduated-a | \n",
+ "
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+ " \n",
+ "
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+ "
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+ "
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+ "
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+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "df"
+ }
+ },
+ "metadata": {},
+ "execution_count": 59
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df['members'] = df['members'].apply(lambda x: x[4:] if x.startswith('-ap-') else x)"
+ ],
+ "metadata": {
+ "id": "-6a_qlCUsvh4"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df.drop(columns=['@members'], inplace=True)"
+ ],
+ "metadata": {
+ "id": "SXHfgstpsyJi"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "suffixes = df['members'].str.split('-').str[-1]\n",
+ "\n",
+ "# Count frequencies of suffixes\n",
+ "suffix_freq = suffixes.value_counts().reset_index()\n",
+ "suffix_freq.columns = ['Suffix', 'Frequency']\n",
+ "\n",
+ "# Sort by frequency\n",
+ "suffix_freq = suffix_freq.sort_values(by='Frequency', ascending=False)\n",
+ "\n",
+ "# Display suffixes ordered by frequency\n",
+ "print(suffix_freq[:40])"
+ ],
+ "metadata": {
+ "id": "m3qrn1yrtDHT",
+ "outputId": "c410f569-75dd-4de3-a906-cf5a775be230",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " Suffix Frequency\n",
+ "0 n 151001\n",
+ "1 a 30150\n",
+ "2 v 25098\n",
+ "3 r 5595\n",
+ "4 1 146\n",
+ "5 2 69\n",
+ "6 s 12\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "filtered_df = df[df['members'].str.endswith(('1', '2', 's'))]\n",
+ "\n",
+ "print(filtered_df)"
+ ],
+ "metadata": {
+ "id": "zZf0TzcLxKhe",
+ "outputId": "b3e6d6fe-6407-42d0-9b09-e4b4506cb646",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " @partOfSpeech @lexfile members\n",
+ "286 n noun.shape lead-n-1\n",
+ "301 n noun.shape bow-n-1\n",
+ "325 n noun.shape tower-n-1\n",
+ "782 s adj.all panelled-s\n",
+ "2303 s adj.all centre-s\n",
+ "... ... ... ...\n",
+ "117472 v verb.body tear-v-2\n",
+ "117596 v verb.body recover-v-1\n",
+ "118299 v verb.communication bow-v-1\n",
+ "118397 v verb.communication bow-v-1\n",
+ "118473 v verb.communication whoop-v-1\n",
+ "\n",
+ "[227 rows x 3 columns]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df['members'] = df['members'].apply(lambda x: x.replace('-ap-', \"'\")) # They use this for apostrophe for some reason, probably because it was stored as yaml"
+ ],
+ "metadata": {
+ "id": "LRCd0Zy_trIB"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# List of suffixes to remove\n",
+ "suffixes_to_remove = ['-n', '-a', '-v', '-r', '-1', '-2', '-s']\n",
+ "\n",
+ "# Function to remove suffixes\n",
+ "def remove_suffixes(member):\n",
+ " # Iterate until no suffixes are left\n",
+ " while any(member.endswith(suffix) for suffix in suffixes_to_remove):\n",
+ " for suffix in suffixes_to_remove:\n",
+ " if member.endswith(suffix):\n",
+ " member = member[:-len(suffix)] # Remove the suffix\n",
+ " return member\n",
+ "\n",
+ "# Apply the function to each member in the DataFrame\n",
+ "df['members'] = df['members'].apply(remove_suffixes)\n",
+ "\n",
+ "# Display the updated DataFrame\n",
+ "df.head()"
+ ],
+ "metadata": {
+ "id": "_dmFfqOwx3X4",
+ "outputId": "94b513b4-31e0-4cb8-8f87-97ca2e656a19",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ }
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " @partOfSpeech @lexfile members\n",
+ "0 a adj.ppl avenged\n",
+ "1 a adj.ppl unavenged\n",
+ "2 a adj.ppl beaten\n",
+ "3 a adj.ppl calibrated\n",
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+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df['members'] = df['members'].apply(lambda x: \" \".join(x.split(\"_\")))"
+ ],
+ "metadata": {
+ "id": "1XbVExgWwaVo"
+ },
+ "execution_count": null,
+ "outputs": []
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+ "df.head()"
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+ "outputId": "f842eccc-d7c3-4479-e7ed-12ddfe0d6afb",
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+ "base_uri": "https://localhost:8080/",
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+ {
+ "output_type": "execute_result",
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+ "text/plain": [
+ " @partOfSpeech @lexfile members\n",
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+ ]
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+ {
+ "cell_type": "code",
+ "source": [
+ "pd.get_dummies(df['@partOfSpeech'])"
+ ],
+ "metadata": {
+ "id": "3VmUnWJel5CK",
+ "outputId": "ee86f84b-2af6-4be2-e251-1d46fa792139",
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