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avidale/convert-conjuction-to-separate-phrases.ipynb

Last active May 25, 2023 21:25
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convert-conjuction-to-separate-phrases.ipynb
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"metadata": {
"colab": {
"name": "convert conjuction to separate phrases",
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"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
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"<a href=\"https://colab.research.google.com/gist/avidale/a8f8e12b5ebef7bf0dbc2694ada0fa57/convert-conjuction-to-separate-phrases.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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{
"cell_type": "code",
"metadata": {
"id": "tnfnoUNWZaE0",
"colab_type": "code",
"colab": {}
},
"source": [
"!python -m spacy download en_core_web_lg"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9uW52IAPZVSM",
"colab_type": "code",
"colab": {}
},
"source": [
" #!python -m spacy download en_core_web_lg\n",
" import spacy\n",
" nlp = spacy.load('en_core_web_lg')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "UWPl5-1QZo4s",
"colab_type": "code",
"colab": {}
},
"source": [
"text1 = 'I like cats and dogs.'\n",
"text2 = 'The steering system shall ensure easy and safe handling of the vehicle up to its maximum design speed or in case of a trailer up to its technically permitted maximum speed.'"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "g1OiO8VCqylp",
"colab_type": "code",
"outputId": "834098a0-340b-48ca-bcab-821b0cef3f6f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 333
}
},
"source": [
"from spacy import displacy\n",
"displacy.render(nlp(text1), style=\"dep\", jupyter=True)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"222.0\">\n",
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"</text>\n",
"\n",
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"222.0\">\n",
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"225\">like</tspan>\n",
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"</text>\n",
"\n",
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"222.0\">\n",
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"400\">cats</tspan>\n",
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"</text>\n",
"\n",
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"</text>\n",
"\n",
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"222.0\">\n",
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"750\">dogs.</tspan>\n",
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" </text>\n",
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"metadata": {
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{
"cell_type": "code",
"metadata": {
"id": "lzDmQCLycAhF",
"colab_type": "code",
"outputId": "8a996127-62d6-4405-c4e9-1eeb426d472e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"spacy.explain('cc'), spacy.explain('conj')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('coordinating conjunction', 'conjunct')"
]
},
"metadata": {
"tags": []
},
"execution_count": 130
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QW9WmmYLavED",
"colab_type": "code",
"outputId": "aea62bfd-4857-4e53-bc8f-e01fc0bffcfa",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 423
}
},
"source": [
"from spacy import displacy\n",
"displacy.render(nlp(text2), style=\"dep\", jupyter=True, options={'compact':True, 'distance': 70})\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"292.0\">\n",
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"\n",
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"\n",
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"</text>\n",
"\n",
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" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"820\">vehicle</tspan>\n",
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"</text>\n",
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"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "j_DnlT3pdrrE",
"colab_type": "code",
"colab": {}
},
"source": [
" import itertools\n",
"\n",
" def generate_trees(root):\n",
" \"\"\"\n",
" Yield all conjuncted variants of subtrees that can be generated from the given node.\n",
" A subtree here is just a set of nodes.\n",
" \"\"\"\n",
" prev_result = [root]\n",
" if not root.children:\n",
" yield prev_result\n",
" return\n",
" \n",
" children_deps = {c.dep_ for c in root.children}\n",
" if 'conj' in children_deps:\n",
" # generate two options: subtree without cc+conj, or with conj child replacing the root\n",
" # the first option:\n",
" good_children = [c for c in root.children if c.dep_ not in {'cc', 'conj'}]\n",
" for subtree in combine_children(prev_result, good_children):\n",
" yield subtree \n",
" # the second option\n",
" for child in root.children:\n",
" if child.dep_ == 'conj':\n",
" for subtree in generate_trees(child):\n",
" yield subtree\n",
" else:\n",
" # otherwise, just combine all the children subtrees\n",
" for subtree in combine_children([root], root.children):\n",
" yield subtree\n",
"\n",
" def combine_children(prev_result, children):\n",
" \"\"\" Combine the parent subtree with all variants of the children subtrees \"\"\"\n",
" child_lists = []\n",
" for child in children:\n",
" cl = list(generate_trees(child))\n",
" child_lists.append(cl)\n",
" for prod in itertools.product(*child_lists): # all possible combinations\n",
" yield prev_result + [tok for parts in prod for tok in parts]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3A9DoNa8elJ1",
"colab_type": "code",
"outputId": "18e842dc-327f-4597-85b9-e0a7335e4df3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
" text = 'I like cats and dogs.'\n",
" doc = nlp(text)\n",
" sentence = list(doc.sents)[0]\n",
" for tree in generate_trees(sentence.root):\n",
" print(' '.join([token.text for token in sorted(tree, key=lambda x: x.i)]))\n",
" # I like cats .\n",
" # I like dogs ."
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"I like cats .\n",
"I like dogs .\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2jlXMKKImbGd",
"colab_type": "code",
"outputId": "86a07aab-b279-43b9-bb99-e5a15d3cc99a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"source": [
" text = 'The steering system shall ensure easy and safe handling of the vehicle up to its maximum design speed or in case of a trailer up to its technically permitted maximum speed.'\n",
" doc = nlp(text)\n",
" sentence = list(doc.sents)[0]\n",
" for tree in generate_trees(sentence.root):\n",
" print(' '.join([token.text for token in sorted(tree, key=lambda x: x.i)]))\n",
" # The steering system shall ensure easy handling of the vehicle up to its maximum design speed .\n",
" # The steering system shall ensure easy handling of the vehicle up in case of a trailer up to its technically permitted maximum speed .\n",
" # The steering system shall ensure safe handling of the vehicle up to its maximum design speed .\n",
" # The steering system shall ensure safe handling of the vehicle up in case of a trailer up to its technically permitted maximum speed ."
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"The steering system shall ensure easy handling of the vehicle up to its maximum design speed .\n",
"The steering system shall ensure easy handling of the vehicle up in case of a trailer up to its technically permitted maximum speed .\n",
"The steering system shall ensure safe handling of the vehicle up to its maximum design speed .\n",
"The steering system shall ensure safe handling of the vehicle up in case of a trailer up to its technically permitted maximum speed .\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-JkOBnDdksDt",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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