2.7 Lemmatization.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.7 Lemmatization\n",
    "\n",
    "Where stemming removes the last few characters of a word, lemmatization stems the word to a more meaningful base form and ensures it does not lose it's meaning. Lemmatization works more intelligently, referencing a pre-defined dictionary containing the context of words and uses this when diminishing the word to the base form. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "connect_tokens = ['connecting', 'connected', 'connectivity', 'connect', 'connects']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_tokens = ['learned', 'learning', 'learn', 'learns', 'learner', 'learners']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "likes_tokens = ['likes', 'better', 'worse']"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create stemmer\n",
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "connecting  :  connect\n",
      "connected  :  connect\n",
      "connectivity  :  connect\n",
      "connect  :  connect\n",
      "connects  :  connect\n"
     ]
    }
   ],
   "source": [
    "for t in connect_tokens:\n",
    "    print(t, \" : \", ps.stem(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learned  :  learn\n",
      "learning  :  learn\n",
      "learn  :  learn\n",
      "learns  :  learn\n",
      "learner  :  learner\n",
      "learners  :  learner\n"
     ]
    }
   ],
   "source": [
    "for t in learn_tokens:\n",
    "    print(t, \" : \", ps.stem(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "likes  :  like\n",
      "better  :  better\n",
      "worse  :  wors\n"
     ]
    }
   ],
   "source": [
    "for t in likes_tokens:\n",
    "    print(t, \" : \", ps.stem(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to /home/lauren/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('wordnet')\n",
    "from nltk.stem import WordNetLemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create lemmatizer \n",
    "lemmatizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in connect_tokens:\n",
    "    print(t, \" : \", lemmatizer.lemmatize(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in learn_tokens:\n",
    "    print(t, \" : \", lemmatizer.lemmatize(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in likes_tokens:\n",
    "    print(t, \" : \", lemmatizer.lemmatize(t))"
   ]
  }
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