TMPreProcessing.ipynb

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   "source": [
    "# Translation Memory Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import nltk"
   ]
  },
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   "execution_count": 2,
   "metadata": {},
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      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/khannatanmai/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
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   "source": [
    "nltk.download('stopwords')"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### English Stopwords"
   ]
  },
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   "execution_count": 3,
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   "outputs": [
    {
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     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "stop_words = stopwords.words('english')\n",
    "print(stop_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing\n",
    "\n",
    "We convert the data to lowercase and remove all stopwords for preprocessing.\n",
    "\n",
    "We then create a Tab Separated Output file as the TM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../tm_data/tm_src.txt') as infile:\n",
    "    with open('../tm_data/tm_src_pp.txt', 'w+') as outfile:\n",
    "        for line in infile:\n",
    "            lower_line = line.lower() #Converting to Lowercase\n",
    "\n",
    "            #print(lower_line)\n",
    "\n",
    "            tokens = word_tokenize(lower_line)\n",
    "\n",
    "            content_words = [word for word in tokens if word not in stop_words] #Removing Stopwords\n",
    "            \n",
    "            outline = ''\n",
    "            \n",
    "            for word in content_words:\n",
    "                outline += word +'\\t' #Creating Tab Separated Output File\n",
    "                \n",
    "            outfile.write(outline + '\\n')\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advantages:\n",
    "- Faster Search and not much loss in accuracy\n",
    "- No need to do tokenisation while searching"
   ]
  }
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