{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1f9b75a-f626-487d-857a-27717aa788bf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install -U pip setuptools wheel\n",
    "!pip install -U spacy\n",
    "!python -m spacy download en_core_web_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f08b8fa4-59d5-4a51-8979-d29aa7915d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "from spacy import displacy\n",
    "from pathlib import Path\n",
    "\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "def highlight_root(sentence):\n",
    "  doc = nlp(sentence)\n",
    "  \n",
    "  # Find the root of the sentence\n",
    "  root = None\n",
    "  for token in doc:\n",
    "    if token.dep_ == 'ROOT':\n",
    "      root = token\n",
    "      break\n",
    "  \n",
    "  # Highlight the root entity in the output\n",
    "  if root is not None:\n",
    "    root_start = root.idx\n",
    "    root_end = root.idx + len(root.text)\n",
    "  \n",
    "    text = [{\n",
    "      'text': sentence,\n",
    "      'ents': [{\n",
    "        'start': root_start,\n",
    "        'end': root_end,\n",
    "        'label': '',\n",
    "      }],\n",
    "      'title': None\n",
    "    }]\n",
    "    displacy.render(text, style='ent', manual=True)\n",
    "  else:\n",
    "      print(\"No root found.\")\n",
    "  return root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d58bfcd-a222-4da2-970c-72326c478861",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"The spectacular aurora light displays that appear in Earth’s atmosphere around the north and south magnetic poles were once mysterious phenomena.\"\n",
    "\n",
    "doc = nlp(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42ee6c93-b37d-494a-82d6-fb97a0f7acba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标注每个单词的词性\n",
    "\n",
    "for token in doc:\n",
    "  print(token.text, token.pos_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "517506f7-240a-4106-a62b-33dc7f86a391",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 高亮标注主句的谓语动词\n",
    "\n",
    "root = highlight_root(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6c5898f-4b0c-4e6e-81b5-0e8231c0e9cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 主句的简化版本\n",
    "\n",
    "children = list(root.children)\n",
    "children.insert(1, root)\n",
    "simplified_setence = ' '.join(str(c) for c in children).strip().replace(\" .\", \".\").capitalize()\n",
    "\n",
    "print(simplified_setence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df071fdb-0618-453a-8482-c818fea98a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 图形化显示句子成分之间的依赖关系\n",
    "\n",
    "displacy.render(doc, style=\"dep\", options={'distance': 60})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "273694e6-2e32-48e6-9317-5353612a8c89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将图形保存为 dep-graph.svg 文件\n",
    "\n",
    "svg = displacy.render(doc, style=\"dep\", jupyter=False)\n",
    "output_path = Path(\"dep-graph.svg\")\n",
    "output_path.open(\"w\", encoding=\"utf-8\").write(svg)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
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