{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "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": "74e1d307-5ca7-4501-ded9-64e83b1074db", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting datasets\n", " Downloading datasets-2.18.0-py3-none-any.whl (510 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: filelock in 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"afe19112e372424086988965f0f34e14", "19d29f06a0224528b958ff1343227823", "55d46c4e9ad04fb9ad0e09ec974124de", "6efc128c6a6049b0bdc45b0f025e20b1", "6be48d20681440488a7106ce7ff8e262", "5e92cc16fc09441a92e64ccef09f1ce7", "3739c6c8081642f1b9ec2b6ff13d924a", "9255806cdfa94d7e995a8c44a97f192d", "ab5c63323f7346529c94f1fb69f724d3", "a6db4efd7025410fa6e5d1532e234eec", "11c8e1f50ec24b549c08328f41872fe7", "b97f9428fd484c5c91fcc004d77a6470", "3bf0e6cd62c643838cd4cbe0f56dc1bd", "fa742c0c6a0c4a77a4628bd4168d5f96", "ed1955550f164dce97ad7e4c37e202bb", "9f04057c52d1494b92c305078faf9488", "51d41e64a4f049e58be20303601b135d", "ec6bd7afcc254bc88b6ea1034deabfc7", "f3927d5cbee44700960812117b897ea6", "152f8b11637749daaf645f4f25c929f1", "3cb439a513e04220853ac6c1a7f384a4", "fb0f5cbcc929459ba9f596a8621caa36", "da6c5d35317648b8862bb6acf6206bf3", "ff8eee65477f4a89bfca6a2c7229e9e0", "83b8acabcda84ad692f0995cda06836a", "e9f599718f694a0e83bbcaee1e7841e2" ] } }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/2.27k [00:00\n", "
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@id@ili@members@partOfSpeech@lexfileDefinitionSynsetRelationExampleILIDefinition@dc:source
0oewn-03159292-ai18097oewn-avenged-aaadj.pplfor which vengeance has been taken[]an avenged injuryNoneNone
1oewn-03159419-ai18098oewn-unavenged-aaadj.pplfor which vengeance has not been taken[]an unavenged murderNoneNone
2oewn-03159554-ai18099oewn-beaten-aaadj.pplformed or made thin by hammering[]beaten goldNoneNone
3oewn-03159654-ai18100oewn-calibrated-a oewn-graduated-aaadj.pplmarked with or divided into degrees[]a calibrated thermometerNoneNone
4oewn-03159804-ai18101oewn-cantering-aaadj.pplriding at a gait between a trot and a gallop[]the cantering soldiersNoneNone
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@members@partOfSpeech@lexfile
0oewn-avenged-aaadj.ppl
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\n" ], "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": 8 } ] }, { "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": "5ad91eea-b6ce-4b52-8e31-bb28eacd7f84", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "False" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "code", "source": [ "members_not_matching_pattern = df[~matches_pattern]\n", "members_not_matching_pattern" ], "metadata": { "id": "jLvqtPRvGeqN", "outputId": "6eca4f82-bf5f-4e7a-f0a3-16cc3573097a", "colab": { "base_uri": "https://localhost:8080/", "height": 424 } }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " @members @partOfSpeech \\\n", "13 oewn-hand-held-a oewn-handheld-a a \n", "42 oewn-re-created-a a \n", "49 oewn-spray-dried-a a \n", "57 oewn-closed-captioned-a a \n", "116 oewn-plane_figure-n oewn-two-dimensional_figure-n n \n", "... ... ... \n", "119954 oewn-real-time_processing-n oewn-real-time_ope... n \n", "119976 oewn-reuptake-n oewn-re-uptake-n n \n", "120005 oewn-slump-n oewn-slack-n oewn-drop-off-n oewn... n \n", "120123 oewn-constant-volume_process-n oewn-isometric_... n \n", "120127 oewn-anti-selection-n oewn-adverse_selection-n n \n", "\n", " @lexfile \n", "13 adj.ppl \n", "42 adj.ppl \n", "49 adj.ppl \n", "57 adj.ppl \n", "116 noun.shape \n", "... ... \n", "119954 noun.process \n", "119976 noun.process \n", "120005 noun.process \n", "120123 noun.process \n", "120127 noun.process \n", "\n", "[6987 rows x 3 columns]" ], "text/html": [ "\n", "
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