5_Stemming_Lemmatization_2.ipynb

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  {
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   "source": [
    "###                     **Stemming and Lemmatization: Exercises**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Run this cell to import all necessary packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "fn0J_mWHtM57",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "#let import necessary libraries and create the object\n",
    "\n",
    "#for nltk\n",
    "import nltk\n",
    "from nltk.stem import PorterStemmer\n",
    "stemmer = PorterStemmer()\n",
    "\n",
    "#downloading all neccessary packages related to nltk\n",
    "nltk.download('all')\n",
    "\n",
    "\n",
    "#for spacy\n",
    "import spacy\n",
    "nlp = spacy.load(\"en_core_web_sm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xCjMD77juhOm"
   },
   "source": [
    "**Exercise1:** \n",
    "- Convert these list of words into base form using Stemming and Lemmatization and observe the transformations\n",
    "- Write a short note on the words that have different base words using stemming and Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5Ytgw50jtM_h",
    "outputId": "47b1cf60-7eb6-449c-d136-01289a08abf0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  |  run\n",
      "painting  |  paint\n",
      "walking  |  walk\n",
      "dressing  |  dress\n",
      "likely  |  like\n",
      "children  |  children\n",
      "whom  |  whom\n",
      "good  |  good\n",
      "ate  |  ate\n",
      "fishing  |  fish\n"
     ]
    }
   ],
   "source": [
    "#using stemming in nltk\n",
    "lst_words = ['running', 'painting', 'walking', 'dressing', 'likely', 'children', 'whom', 'good', 'ate', 'fishing']\n",
    "\n",
    "from nltk.stem import PorterStemmer\n",
    "\n",
    "stemmer = PorterStemmer()\n",
    "for word in lst_words:\n",
    "    print(word,\" | \",stemmer.stem(word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uU7oPj0ftNCP",
    "outputId": "8bf39249-3afb-46a2-9e4e-9fc75be31cc4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running  |  run\n",
      "painting  |  paint\n",
      "walking  |  walk\n",
      "dressing  |  dress\n",
      "likely  |  likely\n",
      "children  |  child\n",
      "who  |  who\n",
      "good  |  good\n",
      "ate  |  eat\n",
      "fishing  |  fishing\n"
     ]
    }
   ],
   "source": [
    "#using lemmatization in spacy\n",
    "\n",
    "doc = nlp(\"running painting walking dressing likely children who good ate fishing\")\n",
    "\n",
    "for token in doc:\n",
    "    print(token,\" | \",token.lemma_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CLjsQGjVye5W"
   },
   "source": [
    "**Exercise2:**\n",
    "\n",
    "- convert the given text into it's base form using both stemming and lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "0weT7a9tygnk"
   },
   "outputs": [],
   "source": [
    "text = \"\"\"Latha is very multi talented girl.She is good at many skills like dancing, running, singing, playing.She also likes eating Pav Bhagi. she has a \n",
    "habit of fishing and swimming too.Besides all this, she is a wonderful at cooking too.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "id": "JP29y7cjygqa",
    "outputId": "6cdc08a3-d313-495a-c0a6-34366848b27e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "latha is veri multi talent girl.sh is good at mani skill like danc , run , sing , playing.sh also like eat pav bhagi . she ha a habit of fish and swim too.besid all thi , she is a wonder at cook too .\n"
     ]
    }
   ],
   "source": [
    "#using stemming in nltk\n",
    "\n",
    "#step1: Word tokenizing\n",
    "all_word_tokens = nltk.word_tokenize(text)\n",
    "\n",
    "\n",
    "#step2: getting the base form for each token using stemmer\n",
    "all_base_words = []\n",
    "\n",
    "for token in all_word_tokens:\n",
    "  base_form = stemmer.stem(token)\n",
    "  all_base_words.append(base_form)\n",
    "\n",
    "\n",
    "#step3: joining all words in a list into string using 'join()'\n",
    "final_base_text = ' '.join(all_base_words)\n",
    "print(final_base_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hysIhJuxyg0B",
    "outputId": "da5f4be7-48ce-44de-a783-866571f796b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latha be very multi talented girl . she be good at many skill like dancing , running , singing , play . she also like eat Pav Bhagi . she have a \n",
      " habit of fishing and swim too . besides all this , she be a wonderful at cook too . \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#using lemmatisation in spacy\n",
    "\n",
    "\n",
    "#step1: Creating the object for the given text\n",
    "doc = nlp(text)\n",
    "all_base_words = []\n",
    "\n",
    "#step2: getting the base form for each token using spacy 'lemma_'\n",
    "for token in doc:\n",
    "  base_word =  token.lemma_\n",
    "  all_base_words.append(base_word)\n",
    "\n",
    "\n",
    "#step3: joining all words in a list into string using 'join()'\n",
    "final_base_text = ' '.join(all_base_words)\n",
    "print(final_base_text)"
   ]
  }
 ],
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