919 lines
51 KiB
Plaintext
919 lines
51 KiB
Plaintext
{
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"cells": [
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "Import Cell\n",
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"id": "dd72d1539056a64"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-21T17:01:35.974978Z",
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"start_time": "2025-11-21T17:01:34.412508Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import token\n",
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"import spacy\n",
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"from spacy import displacy\n",
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"from IPython.display import display, HTML\n",
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"\n",
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"nlp = spacy.load(\"en_core_web_md\") # Medium size model\n",
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"\n",
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"test_sentences = [\n",
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" \"The cat sat on the mat.\",\n",
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" \"On the mat, the cat was sitting.\",\n",
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" \"A completely different sentence about something else.\"\n",
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"]"
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],
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"id": "12579bf734bb1a92",
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"outputs": [],
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"execution_count": 21
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "Keep punctuation for direct copy detection but remove for semantic/keyword based methods",
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"id": "1c26616777253f10"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-20T19:03:29.658876Z",
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"start_time": "2025-11-20T19:03:27.809309Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"for sent in test_sentences:\n",
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" doc = nlp(sent)\n",
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" print(f\"Sentence: {sent}\")\n",
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" print(f\"Tokens: {[token.text for token in doc]}\")\n",
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" print(\"---\")\n"
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],
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"id": "e003ac06a58cfbb4",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sentence: The cat sat on the mat.\n",
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"Tokens: ['The', 'cat', 'sat', 'on', 'the', 'mat', '.']\n",
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"---\n",
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"Sentence: On the mat, the cat was sitting.\n",
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"Tokens: ['On', 'the', 'mat', ',', 'the', 'cat', 'was', 'sitting', '.']\n",
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"---\n",
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"Sentence: A completely different sentence about something else.\n",
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"Tokens: ['A', 'completely', 'different', 'sentence', 'about', 'something', 'else', '.']\n",
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"---\n"
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]
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}
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],
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"execution_count": 17
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-21T23:26:23.172796Z",
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"start_time": "2025-11-21T23:26:21.541542Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"class TextPreprocessor:\n",
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" def __init__(self):\n",
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" self.nlp = spacy.load(\"en_core_web_md\")\n",
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"\n",
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" @staticmethod\n",
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" def direct_detection(text):\n",
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" \"\"\"For direct copy detection\"\"\"\n",
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" #Keep punctuation\n",
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" return text.lower().strip()\n",
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"\n",
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" def semantic_analysis(self, text):\n",
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" \"\"\"Semantic Similarity\"\"\"\n",
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" doc = self.nlp(text)\n",
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" processed_tokens = []\n",
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" # Remove stopwords, punctuation\n",
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" for token in doc:\n",
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" if not token.is_punct and not token.is_space and token.is_alpha and not token.is_stop:\n",
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" processed_tokens.append(token.lemma_.lower())\n",
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" return \" \".join(processed_tokens)\n",
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"\n",
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" def syntactic_analysis(self, text):\n",
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" \"\"\"Syntactic Similarity\"\"\"\n",
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" doc = self.nlp(text)\n",
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" processed_tokens = []\n",
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"\n",
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" # Normalize content words\n",
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" for token in doc:\n",
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" if token.is_space:\n",
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" continue\n",
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" elif token.is_punct:\n",
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" processed_tokens.append(token.text) # Keep punctuation\n",
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" elif token.is_stop:\n",
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" processed_tokens.append(token.lemma_.lower()) # Normalize stopwords\n",
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" else:\n",
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" processed_tokens.append(token.lemma_.lower()) # Normalize content words\n",
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" return \" \".join(processed_tokens)\n",
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"\n",
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"\n",
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"preprocessor = TextPreprocessor()\n",
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"\n",
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"processed_direct = []\n",
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"processed_semantic = []\n",
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"processed_syntactic = []\n",
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"\n",
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"for sentence in test_sentences:\n",
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" print(\"-\" * 50)\n",
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" print(f\"Sentence: {sentence}\")\n",
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" direct = preprocessor.direct_detection(sentence)\n",
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" processed_direct.append(direct)\n",
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" print(\"--- Direct Sentence ---\")\n",
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" print(f\"{direct}\")\n",
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" semantic = preprocessor.semantic_analysis(sentence)\n",
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" processed_semantic.append(semantic)\n",
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" print(\"--- Semantic Sentence ---\")\n",
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" print(f\"{semantic}\")\n",
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" syntactic = preprocessor.syntactic_analysis(sentence)\n",
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" processed_syntactic.append(syntactic)\n",
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" print(\"--- Syntactic Sentence ---\")\n",
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" print(f\"{syntactic}\")\n",
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"\n",
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"print(\"-\" * 50)\n",
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"#for sent in test_sentences:\n",
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"# print(f\"Original Sentence: {sent}\")\n",
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"# print(\"--- Semantic Analysis ---\")\n",
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"# print(f\"Preprocessed Sentence: {preprocessor.semantic_analysis(sent)}\")\n",
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"# print(\"--- Syntactic Analysis ---\")\n",
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"# print(f\"Preprocessed Sentence: {preprocessor.syntactic_analysis(sent)}\")\n",
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"# print(\"-\" * 50)"
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],
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"id": "5e488a878a5cfccb",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------\n",
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"Sentence: The cat sat on the mat.\n",
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"--- Direct Sentence ---\n",
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"the cat sat on the mat.\n",
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"--- Semantic Sentence ---\n",
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"cat sit mat\n",
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"--- Syntactic Sentence ---\n",
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"the cat sit on the mat .\n",
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"--------------------------------------------------\n",
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"Sentence: On the mat, the cat was sitting.\n",
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"--- Direct Sentence ---\n",
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"on the mat, the cat was sitting.\n",
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"--- Semantic Sentence ---\n",
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"mat cat sit\n",
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"--- Syntactic Sentence ---\n",
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"on the mat , the cat be sit .\n",
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"--------------------------------------------------\n",
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"Sentence: A completely different sentence about something else.\n",
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"--- Direct Sentence ---\n",
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"a completely different sentence about something else.\n",
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"--- Semantic Sentence ---\n",
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"completely different sentence\n",
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"--- Syntactic Sentence ---\n",
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"a completely different sentence about something else .\n",
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"--------------------------------------------------\n"
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]
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}
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],
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"execution_count": 37
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-21T23:39:48.443022Z",
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"start_time": "2025-11-21T23:39:48.411766Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"def extract_parse_tree(text):\n",
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" doc = nlp(text)\n",
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"\n",
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" print(f\"Sentence: {text}\")\n",
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" print(\"\\nDependenct Parse Tree:\")\n",
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" print(\"-\" * 50)\n",
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"\n",
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" for token in doc:\n",
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" print(f\"{token.text:<12} {token.dep_:<12} {token.head.text:<12} {[child.text for child in token.children]}\")\n",
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"\n",
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" return doc\n",
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"\n",
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"for sentence in processed_syntactic:\n",
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" doc = extract_parse_tree(sentence)\n",
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" print(\"\\n\" + \"=\"*60 + \"\\n\")"
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],
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"id": "83fc18c9de2e354",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sentence: the cat sit on the mat .\n",
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"\n",
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"Dependenct Parse Tree:\n",
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"--------------------------------------------------\n",
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"the det cat []\n",
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"cat nsubj sit ['the']\n",
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"sit ROOT sit ['cat', 'on', '.']\n",
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"on prep sit ['mat']\n",
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"the det mat []\n",
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"mat pobj on ['the']\n",
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". punct sit []\n",
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"\n",
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"============================================================\n",
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"\n",
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"Sentence: on the mat , the cat be sit .\n",
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"\n",
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"Dependenct Parse Tree:\n",
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"--------------------------------------------------\n",
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"on prep sit ['mat']\n",
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"the det mat []\n",
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"mat pobj on ['the']\n",
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", punct sit []\n",
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"the det cat []\n",
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"cat nsubj sit ['the']\n",
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"be aux sit []\n",
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"sit ROOT sit ['on', ',', 'cat', 'be', '.']\n",
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". punct sit []\n",
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"\n",
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"============================================================\n",
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"\n",
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"Sentence: a completely different sentence about something else .\n",
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"\n",
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"Dependenct Parse Tree:\n",
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"--------------------------------------------------\n",
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"a det sentence []\n",
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"completely advmod different []\n",
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"different amod sentence ['completely']\n",
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"sentence ROOT sentence ['a', 'different', 'about', '.']\n",
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"about prep sentence ['something']\n",
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"something pobj about ['else']\n",
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"else advmod something []\n",
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". punct sentence []\n",
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"\n",
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"============================================================\n",
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"\n"
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]
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}
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|
],
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"execution_count": 39
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},
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{
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|
"metadata": {},
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"cell_type": "markdown",
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"source": "***USE NetworkX",
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|
"id": "5b5c8742d7c4c4c5"
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|
},
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|
{
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|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-11-21T18:20:09.575176Z",
|
|
"start_time": "2025-11-21T18:20:09.465504Z"
|
|
}
|
|
},
|
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"cell_type": "code",
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"source": [
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"\n",
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"\n",
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"def visualize_parse_tree(text):\n",
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" doc = nlp(text)\n",
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" html = displacy.render(doc, style=\"dep\", jupyter=False, options={\"distance\": 100})\n",
|
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" display(HTML(html))\n",
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"\n",
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"\n",
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"\n",
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"for sentence in test_sentences:\n",
|
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" print(f\"Sentence: {sentence}\")\n",
|
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" print(\"---\")\n",
|
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" processed_sentence = preprocessor.syntactic_analysis(sentence)\n",
|
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" print(f\"Processed Sentence: \" + processed_sentence)\n",
|
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" visualize_parse_tree(processed_sentence)\n",
|
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" visualize_parse_tree(sentence)"
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],
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"id": "e413238c1af12f62",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sentence: The cat sat on the mat.\n",
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"---\n",
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"Processed Sentence: the cat sit on the mat .\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<IPython.core.display.HTML object>"
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],
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"text/html": [
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"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xml:lang=\"en\" id=\"cbdcc1a7b2c3434da049b33ce22ba9dc-0\" class=\"displacy\" width=\"650\" height=\"237.0\" direction=\"ltr\" style=\"max-width: none; height: 237.0px; color: #000000; background: #ffffff; font-family: Arial; direction: ltr\">\n",
|
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"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"147.0\">\n",
|
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" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">the</tspan>\n",
|
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" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">DET</tspan>\n",
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"</text>\n",
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"\n",
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"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"147.0\">\n",
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" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"150\">cat</tspan>\n",
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" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"150\">NOUN</tspan>\n",
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"</text>\n",
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"\n",
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"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"147.0\">\n",
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" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"250\">sit</tspan>\n",
|
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" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"250\">VERB</tspan>\n",
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"</text>\n",
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"\n",
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"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"147.0\">\n",
|
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" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"350\">on</tspan>\n",
|
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" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"350\">ADP</tspan>\n",
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"</text>\n",
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"\n",
|
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"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"147.0\">\n",
|
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" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"450\">the</tspan>\n",
|
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" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"450\">DET</tspan>\n",
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"</text>\n",
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"\n",
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"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"147.0\">\n",
|
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" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"550\">mat .</tspan>\n",
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" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"550\">NOUN</tspan>\n",
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"</text>\n",
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"\n",
|
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"<g class=\"displacy-arrow\">\n",
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" <path class=\"displacy-arc\" id=\"arrow-cbdcc1a7b2c3434da049b33ce22ba9dc-0-0\" stroke-width=\"2px\" d=\"M70,102.0 C70,52.0 145.0,52.0 145.0,102.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
|
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" <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
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" <textPath xlink:href=\"#arrow-cbdcc1a7b2c3434da049b33ce22ba9dc-0-0\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">det</textPath>\n",
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" </text>\n",
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" <path class=\"displacy-arrowhead\" d=\"M70,104.0 L62,92.0 78,92.0\" fill=\"currentColor\"/>\n",
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"</g>\n",
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"\n",
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"<g class=\"displacy-arrow\">\n",
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" <path class=\"displacy-arc\" id=\"arrow-cbdcc1a7b2c3434da049b33ce22ba9dc-0-1\" stroke-width=\"2px\" d=\"M170,102.0 C170,52.0 245.0,52.0 245.0,102.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
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" <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
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" <textPath xlink:href=\"#arrow-cbdcc1a7b2c3434da049b33ce22ba9dc-0-1\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">nsubj</textPath>\n",
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" </text>\n",
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" <path class=\"displacy-arrowhead\" d=\"M170,104.0 L162,92.0 178,92.0\" fill=\"currentColor\"/>\n",
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"</g>\n",
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"\n",
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"<g class=\"displacy-arrow\">\n",
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" <path class=\"displacy-arc\" id=\"arrow-cbdcc1a7b2c3434da049b33ce22ba9dc-0-2\" stroke-width=\"2px\" d=\"M270,102.0 C270,52.0 345.0,52.0 345.0,102.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
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" <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
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" <textPath xlink:href=\"#arrow-cbdcc1a7b2c3434da049b33ce22ba9dc-0-2\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">prep</textPath>\n",
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" </text>\n",
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" <path class=\"displacy-arrowhead\" d=\"M345.0,104.0 L353.0,92.0 337.0,92.0\" fill=\"currentColor\"/>\n",
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"</g>\n",
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"\n",
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"<g class=\"displacy-arrow\">\n",
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" <path class=\"displacy-arc\" id=\"arrow-cbdcc1a7b2c3434da049b33ce22ba9dc-0-3\" stroke-width=\"2px\" d=\"M470,102.0 C470,52.0 545.0,52.0 545.0,102.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
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" <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
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" <textPath xlink:href=\"#arrow-cbdcc1a7b2c3434da049b33ce22ba9dc-0-3\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">det</textPath>\n",
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|
" </text>\n",
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" <path class=\"displacy-arrowhead\" d=\"M470,104.0 L462,92.0 478,92.0\" fill=\"currentColor\"/>\n",
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"</g>\n",
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"\n",
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"<g class=\"displacy-arrow\">\n",
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" <path class=\"displacy-arc\" id=\"arrow-cbdcc1a7b2c3434da049b33ce22ba9dc-0-4\" stroke-width=\"2px\" d=\"M370,102.0 C370,2.0 550.0,2.0 550.0,102.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
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" <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
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" <textPath xlink:href=\"#arrow-cbdcc1a7b2c3434da049b33ce22ba9dc-0-4\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">pobj</textPath>\n",
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" </text>\n",
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" <path class=\"displacy-arrowhead\" d=\"M550.0,104.0 L558.0,92.0 542.0,92.0\" fill=\"currentColor\"/>\n",
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"</g>\n",
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|
"</svg>"
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|
]
|
|
},
|
|
"metadata": {},
|
|
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