{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tokenisation\n",
    "\n",
    "The term *tokenisation* refers to the process of breaking down a text or sentence into smaller units called *tokens*. In `lambeq` these tokens correspond to words, since the parser needs to know exactly what kind of words or symbols and punctuation marks are included in the sentence in order to provide an accurate grammatical analysis.\n",
    "\n",
    "## Word tokenisation\n",
    "\n",
    "By default, Bobcat parser assumes that every word in a sentence is delimited by a whitespace, as below:\n",
    "\n",
    "```\n",
    "\"John gave Mary a flower\"\n",
    "```\n",
    "   \n",
    "Note however that when working with raw text, this is rarely the case. Consider for example the sentence:\n",
    "\n",
    "```\n",
    "\"This sentence isn't worth £100 (or is it?).\"\n",
    "```\n",
    "   \n",
    "A naïve tokenisation based on white spaces would result in the following list of tokens:\n",
    "\n",
    "```\n",
    "[\"This\", \"sentence\", \"isn't\", \"worth\", \"£100\", \"(or\", \"is\", \"it?).\"]\n",
    "```\n",
    "   \n",
    "missing, for example, that \"isn't\" represents actually two words and \"(or\" is not a proper word. \n",
    "\n",
    "In `lambeq`, tokenisation is provided through the `Tokeniser` class hierarcy, and specifically by using the `SpacyTokeniser` class, based on the popular NLP package [SpaCy](<https://spacy.io>). Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This',\n",
       " 'sentence',\n",
       " 'is',\n",
       " \"n't\",\n",
       " 'worth',\n",
       " '£',\n",
       " '100',\n",
       " '(',\n",
       " 'or',\n",
       " 'is',\n",
       " 'it',\n",
       " '?',\n",
       " ')',\n",
       " '.']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lambeq import SpacyTokeniser\n",
    "\n",
    "tokeniser = SpacyTokeniser()\n",
    "sentence = \"This sentence isn't worth £100 (or is it?).\"\n",
    "tokens = tokeniser.tokenise_sentence(sentence)\n",
    "tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then pass the list of the tokens to the parser, setting the `tokenised` argument of the `BobcatParser.sentence2diagram` method to True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2800x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lambeq import BobcatParser\n",
    "\n",
    "parser = BobcatParser(verbose='suppress')\n",
    "diagram = parser.sentence2diagram(tokens, tokenised=True)\n",
    "\n",
    "diagram.draw(figsize=(28, 7), fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "To tokenise many sentences at once, use the `SpacyTokeniser.tokenise_sentences` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['This', 'is', 'a', 'sentence', '.'],\n",
       " ['This', 'is', '(', 'another', ')', 'sentence', '!']]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences = [\"This is a sentence.\", \"This is (another) sentence!\"]\n",
    "\n",
    "tok_sentences = tokeniser.tokenise_sentences(sentences)\n",
    "tok_sentences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "## Splitting a document into sentences\n",
    "\n",
    "Finally, `lambeq` provides tokenisation at the sentence-level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I love pizza.', 'It is my favorite food.', 'I could eat it every day!']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = \"I love pizza. It is my favorite food. I could eat it every day!\"\n",
    "sentences = tokeniser.split_sentences(text)\n",
    "sentences"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}