ex06.ipynb

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   "source": [
    "# Exercise 06: store annotated text as JSON files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much of the preceding NLP code has been worked into a small library, and we'll call functions from that library to help keep these notebooks more readable. Take a look at the source code in `pynlp.py`, and an example usage:"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "import pynlp\n",
    "\n",
    "html_file = \"html/article1.html\"\n",
    "json_file = \"a1.json\"\n",
    "\n",
    "pynlp.full_parse(html_file, json_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That extracts text from HTML in the first article, then stores the parsed and annotated text as JSON, one line per sentence. Let's look at the first two sentences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sx more a1.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now your turn.** In the following code block, run the extract/parse/save-to-JSON for each of the example HTML files, not just the `article1.html` example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "html_file = \"html/article4.html\"\n",
    "json_file = \"a4.json\"\n",
    "\n",
    "pynlp.full_parse(html_file, json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sx more a4.json"
   ]
  }
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