{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "All the IPython Notebooks in **[Python Natural Language Processing](https://github.com/milaan9/Python_Python_Natural_Language_Processing)** lecture series by **[Dr. Milaan Parmar](https://www.linkedin.com/in/milaanparmar/)** are available @ **[GitHub](https://github.com/milaan9)**\n", "" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "2mc2QuFzHACp" }, "source": [ "# 01 Tokenization by Python" ] }, { "cell_type": "markdown", "metadata": { "id": "5tkJYfGNQs39" }, "source": [ "- **Tokenization is a way of separating a piece of text into smaller units called tokens. Here, tokens can be either words, characters, or subwords.**\n", "- **Tokenization is the act of breaking up a sequence of strings into pieces such as words, keywords, phrases, symbols and other elements called tokens. Tokens can be individual words, phrases or even whole sentences. In the process of tokenization, some characters like punctuation marks are discarded.**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aIr4gmR1ebiw", "outputId": "445027b3-a826-4950-f8e0-043fd259f5b0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['I', 'll', 'always', 'be', 'there', 'with', 'you', 'forever', 'in', 'your', 'heart', '']\n" ] } ], "source": [ "# Split by Whitespace\n", "\n", "import re\n", "text = \"I\\'ll always be there with you forever in your heart.!\"\n", "words = re.split(r'\\W+', text)\n", "print(words[:100])" ] }, { "cell_type": "markdown", "metadata": { "id": "4nlEszKsHACv" }, "source": [ "Here It didn't recognise **`.`** at the last of the sentence." ] }, { "cell_type": "markdown", "metadata": { "id": "PF44c1m4ITRu" }, "source": [ "**Remove punctuations and separate the word**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HneykDD2gC4k", "outputId": "81314e08-1e56-4f4a-fe0c-8052a3dad513" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Ill', 'always', 'be', 'there', 'with', 'you', 'forever', 'in', 'your', 'heart']\n" ] } ], "source": [ "import string\n", "import re\n", "\n", "# split into words by white space\n", "words = text.split()\n", "\n", "# prepare regex for char filtering\n", "re_punc = re.compile('[%s]' % re.escape(string.punctuation))\n", "\n", "# remove punctuation from each word\n", "stripped = [re_punc.sub('', w) for w in words]\n", "print(stripped[:100])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nlE9FFEXgDEs", "outputId": "c2c01f66-7d5e-4153-decb-ec211bfe9062" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\"I'll\", 'always', 'be', 'there', 'with', 'you', 'forever', 'in', 'your', 'heart!']\n" ] } ], "source": [ "# string.printable inverse of string.punctuation\n", "\n", "re_print = re.compile('[^%s]' % re.escape(string.printable))\n", "result = [re_print.sub('', w) for w in words]\n", "print(result)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Uf_8fwtrgDRT", "outputId": "d9be8202-6c2f-4641-9012-c48335da0725" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\"i'll\", 'always', 'be', 'there', 'with', 'you', 'forever', 'in', 'your', 'heart!']\n" ] } ], "source": [ "# Normalizing Case\n", "\n", "# split into words by white space\n", "words = text.split()\n", "# convert to lower case\n", "words = [word.lower() for word in words]\n", "print(words[:100])" ] }, { "cell_type": "markdown", "metadata": { "id": "XC1YBN3qJZuv" }, "source": [ "# Spacy\n", "\n", "Spacy is an open-source software python library used in advanced natural language processing and machine learning. It will be used to build information extraction, natural language understanding systems, and to pre-process text for deep learning." ] }, { "cell_type": "markdown", "metadata": { "id": "AQfx-EN2wjhz" }, "source": [ "Install:\n", "```py\n", "!pip install -U spacy\n", "!python -m spacy download en_core_web_sm\n", "```\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ND0h9KA5eirK", "outputId": "127a139a-72ab-4bf9-8b01-730743f9a57b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: spacy in /usr/local/lib/python3.7/dist-packages (2.2.4)\n", "Collecting spacy\n", " Downloading spacy-3.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.0 MB)\n", "\u001b[K |████████████████████████████████| 6.0 MB 8.3 MB/s \n", "\u001b[?25hRequirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.11.3)\n", "Requirement already 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langcodes\n", " Building wheel for langcodes (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for langcodes: filename=langcodes-3.2.1-py3-none-any.whl size=169396 sha256=9531f6c74ca24a2e58cea16da88a53faefff68155c8068bb2c3399a6b8c5a15f\n", " Stored in directory: /root/.cache/pip/wheels/12/9c/b3/d42c928e622075d3b6056733125190086e44c9230878e6eb2b\n", "Successfully built langcodes\n", "Installing collected packages: catalogue, typer, srsly, pydantic, thinc, spacy-loggers, spacy-legacy, pathy, langcodes, spacy\n", " Attempting uninstall: catalogue\n", " Found existing installation: catalogue 1.0.0\n", " Uninstalling catalogue-1.0.0:\n", " Successfully uninstalled catalogue-1.0.0\n", " Attempting uninstall: srsly\n", " Found existing installation: srsly 1.0.5\n", " Uninstalling srsly-1.0.5:\n", " Successfully uninstalled srsly-1.0.5\n", " Attempting uninstall: thinc\n", " Found existing installation: thinc 7.4.0\n", " Uninstalling thinc-7.4.0:\n", " Successfully uninstalled 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"\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n", "You can now load the package via spacy.load('en_core_web_sm')\n" ] } ], "source": [ "!pip install -U spacy\n", "!python -m spacy download en_core_web_sm\n", "\n", "import spacy\n", "nlp = spacy.load('en_core_web_sm')\n", "# Here en_core means core english,web_sm means small language,We import core english language here.\n", "# This is spacy's internal english language" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kMNC9Bg5em1H", "outputId": "5e20c9e6-e9ff-41b4-9535-20ba7a5713fd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"I'll always be there with you forever in your mind!\"\n" ] } ], "source": [ "string = '\"I\\'ll always be there with you forever in your mind!\"'\n", "print(string)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7lBckJcXe0qH", "outputId": "058663e8-0bdc-4a7b-86d1-bc4f99ab795f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\" | I | 'll | always | be | there | with | you | forever | in | your | mind | ! | \" | " ] } ], "source": [ "# Here we will break the string into token and print in text\n", "\n", "doc = nlp(string)\n", "for token in doc:\n", " print(token.text, end=' | ')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZkWH-w-BKlXk", "outputId": "8276135f-a56e-4298-b9c1-ee384756a8f5" }, "outputs": [ { "data": { "text/plain": [ "\"I'll always be there with you forever in your mind!\"" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "doc" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sV4C5FwUe9AW", "outputId": "6dbb350a-4e5a-4843-8726-9dd098c69d1a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I\n", "'m\n", "always\n", "here\n", "to\n", "help\n", "you\n", "all\n", "!\n", "Email:milaanparmar9@gmail.com\n", "or\n", "visit\n", "more\n", "at\n", "https://github.com/milaan9\n", "!\n" ] } ], "source": [ "# Here we break the string into unicode token\n", "\n", "doc2 = nlp(u\"I'm always here to help you all! Email:milaanparmar9@gmail.com or visit more at https://github.com/milaan9!\")\n", "for t in doc2:\n", " print(t)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HzJHS44WfRRW", "outputId": "8c1953f2-912a-46cb-dcb8-58c24d3b1581" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "5\n", "km\n", "NYC\n", "cab\n", "ride\n", "costs\n", "$\n", "10.30\n" ] } ], "source": [ "doc3 = nlp(u'A 5km NYC cab ride costs $10.30')\n", "for t in doc3:\n", " print(t)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "c5Sg8JJsfdBF", "outputId": "95dd0a33-9ac3-4eca-e167-6b7bd5b93a33" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Let\n", "'s\n", "visit\n", "St.\n", "Louis\n", "in\n", "the\n", "U.S.\n", "next\n", "year\n", ".\n" ] } ], "source": [ "doc4 = nlp(u\"Let's visit St. Louis in the U.S. next year.\")\n", "for t in doc4:\n", " print(t)" ] }, { "cell_type": "markdown", "metadata": { "id": "aaqmKOO5MKc3" }, "source": [ "**see the number of word used in the string**" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xkCo6hyBffhl", "outputId": "3d43245a-4cc9-4025-b955-431d6c69a234" }, "outputs": [ { "data": { "text/plain": [ "11" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(doc4)" ] }, { "cell_type": "markdown", "metadata": { "id": "dQq3QuY_MRtt" }, "source": [ "**see the number of vocabolary or character used in the string**" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Gt_7rMkOfhmk", "outputId": "264316cf-4211-4ce4-af09-562b73711486" }, "outputs": [ { "data": { "text/plain": [ "802" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# How many vocabolary present in token\n", "len(doc.vocab)" ] }, { "cell_type": "markdown", "metadata": { "id": "jRUxCMlEMb95" }, "source": [ "**Print the 3rd word from the string**" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wgwP4IoZfi-E", "outputId": "1c09b033-3e52-4367-98b8-bb2dd08a406e" }, "outputs": [ { "data": { "text/plain": [ "better" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "doc5 = nlp(u'It is better to give than to receive.')\n", "# Retrieve the third token:\n", "doc5[2]" ] }, { "cell_type": "markdown", "metadata": { "id": "HI9TiGloMkI2" }, "source": [ "**Print the words from 3rd word to 4th word**" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OtyHwNwxflgF", "outputId": "f6c130f3-f898-4c98-ca14-aff923002e79" }, "outputs": [ { "data": { "text/plain": [ "better to give" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Retrieve three tokens from the middle:\n", "doc5[2:5]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dQa1r-iWfnNE", "outputId": "b6b307d6-5fc8-4e71-ed73-125d0aec78d0" }, "outputs": [ { "data": { "text/plain": [ "than to receive." ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Retrieve the last four tokens:\n", "doc5[-4:]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "otu1NnvZfpiU" }, "outputs": [], "source": [ "doc6 = nlp(u'My dinner was horrible.')\n", "doc7 = nlp(u'Your dinner was delicious.')" ] }, { "cell_type": "markdown", "metadata": { "id": "3Evq_cTRM2pO" }, "source": [ "**We can't store any word from one line to the any word of other line/sentence**" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 180 }, "id": "6UNwsoHIfri0", "outputId": "d8acdb4c-66a6-45e1-a2f2-52a449644076" }, "outputs": [ { "ename": "TypeError", "evalue": "ignored", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Try to change \"My dinner was horrible\" to \"My dinner was delicious\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdoc6\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdoc7\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: 'spacy.tokens.doc.Doc' object does not support item assignment" ] } ], "source": [ "# Try to change \"My dinner was horrible\" to \"My dinner was delicious\"\n", "doc6[3] = doc7[3]" ] }, { "cell_type": "markdown", "metadata": { "id": "0NeK7IKK2wJf" }, "source": [ "### Explain which type of token is this" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FAwFVwGAftIk", "outputId": "9bd6593f-455b-44e9-aba0-cc84d7f8d37e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Apple | to | build | a | Hong | Kong | factory | for | $ | 6 | million | \n", "----\n", "Apple - ORG - Companies, agencies, institutions, etc.\n", "Hong Kong - GPE - Countries, cities, states\n", "$6 million - MONEY - Monetary values, including unit\n" ] } ], "source": [ "# We can explain which type of token is this\n", "doc8 = nlp(u'Apple to build a Hong Kong factory for $6 million')\n", "\n", "for token in doc8:\n", " print(token.text, end=' | ')\n", "\n", "print('\\n----')\n", "# ents shows all entities present in all tokens\n", "for ent in doc8.ents:\n", " print(ent.text+' - '+ent.label_+' - '+str(spacy.explain(ent.label_)))" ] }, { "cell_type": "markdown", "metadata": { "id": "IoslO9CoHAC5" }, "source": [ "Here it shows \"Apple\" may be a companies,agencies,institution.Similarly like Hong Kong may be a countries,cities.....etc" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sbba5DeCfvm0", "outputId": "4507f984-8099-4e11-ea14-9869bbb36d02" }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(doc8.ents)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "93CItEYtfx3U", "outputId": "de022ab5-0f72-4747-9554-f0d9c001e42e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Autonomous cars\n", "insurance liability\n", "manufacturers\n" ] } ], "source": [ "doc9 = nlp(u\"Autonomous cars shift insurance liability toward manufacturers.\")\n", "# noun_chunks finds all noun present in all tokens\n", "for chunk in doc9.noun_chunks:\n", " print(chunk.text)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_I8g6wx0fz50", "outputId": "ccee1ad2-1b57-4915-c00b-21c99ddb3ace" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Red cars\n", "higher insurance rates\n" ] } ], "source": [ "doc10 = nlp(u\"Red cars do not carry higher insurance rates.\")\n", "\n", "for chunk in doc10.noun_chunks:\n", " print(chunk.text)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tQCHFOLIf1TD", "outputId": "a9a8beba-02ad-4ef0-d73b-fe4f66c1265e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "He\n", "a one-eyed, one-horned, flying, purple people-eater\n" ] } ], "source": [ "doc11 = nlp(u\"He was a one-eyed, one-horned, flying, purple people-eater.\")\n", "\n", "for chunk in doc11.noun_chunks:\n", " print(chunk.text)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 398 }, "id": "YOFRWbWkf2jj", "outputId": "0ed4f197-52d6-49fd-9311-768efc9e0167" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " Apple\n", " PROPN\n", "\n", "\n", "\n", " is\n", " AUX\n", "\n", "\n", "\n", " going\n", " VERB\n", "\n", "\n", "\n", " to\n", " PART\n", "\n", "\n", "\n", " build\n", " VERB\n", "\n", "\n", "\n", " a\n", " DET\n", "\n", "\n", "\n", " U.K.\n", " PROPN\n", "\n", "\n", "\n", " factory\n", " NOUN\n", "\n", "\n", "\n", " for\n", " ADP\n", "\n", "\n", "\n", " $\n", " SYM\n", "\n", "\n", "\n", " 6\n", " NUM\n", "\n", "\n", "\n", " million.\n", " NUM\n", "\n", "\n", "\n", " \n", " \n", " nsubj\n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " aux\n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " aux\n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " xcomp\n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " det\n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " compound\n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " dobj\n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " prep\n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " quantmod\n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " compound\n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " pobj\n", " \n", " \n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Here we can see how \"displacy render\" shows which type of token are these\n", "# and relationships between them \n", "from spacy import displacy\n", "\n", "doc = nlp(u'Apple is going to build a U.K. factory for $6 million.')\n", "displacy.render(doc, style='dep', jupyter=True, options={'distance': 110})" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 52 }, "id": "31zs1BD1f4oj", "outputId": "c8fc1643-5286-4a23-d4f4-88f333290912" }, "outputs": [ { "data": { "text/html": [ "
Over \n", "\n", " the last quarter\n", " DATE\n", "\n", " \n", "\n", " Apple\n", " ORG\n", "\n", " sold \n", "\n", " nearly 20 thousand\n", " CARDINAL\n", "\n", " \n", "\n", " iPods\n", " PRODUCT\n", "\n", " for a profit of \n", "\n", " $6 million\n", " MONEY\n", "\n", ".
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Showing types of token and there relationships in a line\n", "doc = nlp(u'Over the last quarter Apple sold nearly 20 thousand iPods for a profit of $6 million.')\n", "displacy.render(doc, style='ent', jupyter=True)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Cp9FS2jif6Mz", "outputId": "4c43ccd9-d089-49dd-c70b-ae148ce08365" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Using the 'dep' visualizer\n", "Serving on http://0.0.0.0:5000 ...\n", "\n", "Shutting down server on port 5000.\n" ] } ], "source": [ "doc = nlp(u'This is a sentence.')\n", "displacy.serve(doc, style='dep')\n", "\n", "# In-case ▶ runtime is too long press ■ Stop " ] }, { "cell_type": "markdown", "metadata": { "id": "nzvL0nQM1DtC" }, "source": [ "# **Tokenization - KN**" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vazxHZOeKtBo", "outputId": "5b226f29-24fc-4e5f-e49b-6d7635ff1841" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n", "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk) (1.15.0)\n" ] } ], "source": [ "!pip install nltk" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "G9kpihDJ1ONU", "outputId": "63c7e578-9fb9-4761-a425-66a42adf21a7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[nltk_data] Downloading collection 'all'\n", "[nltk_data] | \n", "[nltk_data] | Downloading package abc to /root/nltk_data...\n", "[nltk_data] | Unzipping corpora/abc.zip.\n", "[nltk_data] | Downloading package alpino to /root/nltk_data...\n", "[nltk_data] | Unzipping corpora/alpino.zip.\n", 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stemmers/porter_test.zip.\n", "[nltk_data] | Downloading package wmt15_eval to /root/nltk_data...\n", "[nltk_data] | Unzipping models/wmt15_eval.zip.\n", "[nltk_data] | Downloading package mwa_ppdb to /root/nltk_data...\n", "[nltk_data] | Unzipping misc/mwa_ppdb.zip.\n", "[nltk_data] | \n", "[nltk_data] Done downloading collection all\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Tokenization of paragraphs/sentences\n", "import nltk\n", "# nltk.download(\"popular\") # use this to download all popular libraries in nltk\n", "nltk.download('all')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "id": "TEzGQqAV1OnN" }, "outputs": [], "source": [ "paragraph = \"\"\"I have three visions for India. In 3000 years of our history, people from all over \n", " the world have come and invaded us, captured our lands, conquered our minds. \n", " From Alexander onwards, the Greeks, the Turks, the Moguls, the Portuguese, the British,\n", " the French, the Dutch, all of them came and looted us, took over what was ours. \n", " Yet we have not done this to any other nation. We have not conquered anyone. \n", " We have not grabbed their land, their culture, \n", " their history and tried to enforce our way of life on them. \n", " Why? Because we respect the freedom of others.That is why my \n", " first vision is that of freedom. I believe that India got its first vision of \n", " this in 1857, when we started the War of Independence. It is this freedom that\n", " we must protect and nurture and build on. If we are not free, no one will respect us.\n", " My second vision for India’s development. For fifty years we have been a developing nation.\n", " It is time we see ourselves as a developed nation. We are among the top 5 nations of the world\n", " in terms of GDP. We have a 10 percent growth rate in most areas. Our poverty levels are falling.\n", " Our achievements are being globally recognised today. Yet we lack the self-confidence to\n", " see ourselves as a developed nation, self-reliant and self-assured. Isn’t this incorrect?\n", " I have a third vision. India must stand up to the world. Because I believe that unless India \n", " stands up to the world, no one will respect us. Only strength respects strength. We must be \n", " strong not only as a military power but also as an economic power. Both must go hand-in-hand. \n", " My good fortune was to have worked with three great minds. Dr. Vikram Sarabhai of the Dept. of \n", " space, Professor Satish Dhawan, who succeeded him and Dr. Brahm Prakash, father of nuclear material.\n", " I was lucky to have worked with all three of them closely and consider this the great opportunity of my life. \n", " I see four milestones in my career\"\"\"\n", " " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "id": "PT4J91Ql1Okd" }, "outputs": [], "source": [ "# Tokenizing sentences\n", "sentences = nltk.sent_tokenize(paragraph)\n", "\n", "# Tokenizing words\n", "words = nltk.word_tokenize(paragraph)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vlJeJOjj1Ofg", "outputId": "2c62504e-e25a-4e6d-d155-ce2dc046c7fe" }, "outputs": [ { "data": { "text/plain": [ "['I have three visions for India.',\n", " 'In 3000 years of our history, people from all over \\n the world have come and invaded us, captured our lands, conquered our minds.',\n", " 'From Alexander onwards, the Greeks, the Turks, the Moguls, the Portuguese, the British,\\n the French, the Dutch, all of them came and looted us, took over what was ours.',\n", " 'Yet we have not done this to any other nation.',\n", " 'We have not conquered anyone.',\n", " 'We have not grabbed their land, their culture, \\n their history and tried to enforce our way of life on them.',\n", " 'Why?',\n", " 'Because we respect the freedom of others.That is why my \\n first vision is that of freedom.',\n", " 'I believe that India got its first vision of \\n this in 1857, when we started the War of Independence.',\n", " 'It is this freedom that\\n we must protect and nurture and build on.',\n", " 'If we are not free, no one will respect us.',\n", " 'My second vision for India’s development.',\n", " 'For fifty years we have been a developing nation.',\n", " 'It is time we see ourselves as a developed nation.',\n", " 'We are among the top 5 nations of the world\\n in terms of GDP.',\n", " 'We have a 10 percent growth rate in most areas.',\n", " 'Our poverty levels are falling.',\n", " 'Our achievements are being globally recognised today.',\n", " 'Yet we lack the self-confidence to\\n see ourselves as a developed nation, self-reliant and self-assured.',\n", " 'Isn’t this incorrect?',\n", " 'I have a third vision.',\n", " 'India must stand up to the world.',\n", " 'Because I believe that unless India \\n stands up to the world, no one will respect us.',\n", " 'Only strength respects strength.',\n", " 'We must be \\n strong not only as a military power but also as an economic power.',\n", " 'Both must go hand-in-hand.',\n", " 'My good fortune was to have worked with three great minds.',\n", " 'Dr. Vikram Sarabhai of the Dept.',\n", " 'of \\n space, Professor Satish Dhawan, who succeeded him and Dr. Brahm Prakash, father of nuclear material.',\n", " 'I was lucky to have worked with all three of them closely and consider this the great opportunity of my life.',\n", " 'I see four milestones in my career']" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sentences" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "578Z4zGd1ObF", "outputId": "03b50f2b-c02b-4ef2-b706-4d249cfa1a84" }, "outputs": [ { "data": { "text/plain": [ "['I',\n", " 'have',\n", " 'three',\n", " 'visions',\n", " 'for',\n", " 'India',\n", " '.',\n", " 'In',\n", " '3000',\n", " 'years',\n", " 'of',\n", " 'our',\n", " 'history',\n", " ',',\n", " 'people',\n", " 'from',\n", " 'all',\n", " 'over',\n", " 'the',\n", " 'world',\n", " 'have',\n", " 'come',\n", " 'and',\n", " 'invaded',\n", " 'us',\n", " ',',\n", " 'captured',\n", " 'our',\n", " 'lands',\n", " ',',\n", " 'conquered',\n", " 'our',\n", " 'minds',\n", " '.',\n", " 'From',\n", " 'Alexander',\n", " 'onwards',\n", " ',',\n", " 'the',\n", " 'Greeks',\n", " ',',\n", " 'the',\n", " 'Turks',\n", " ',',\n", " 'the',\n", " 'Moguls',\n", " ',',\n", " 'the',\n", " 'Portuguese',\n", " ',',\n", " 'the',\n", " 'British',\n", " ',',\n", " 'the',\n", " 'French',\n", " ',',\n", " 'the',\n", " 'Dutch',\n", " ',',\n", " 'all',\n", " 'of',\n", " 'them',\n", " 'came',\n", " 'and',\n", " 'looted',\n", " 'us',\n", " ',',\n", " 'took',\n", " 'over',\n", " 'what',\n", " 'was',\n", " 'ours',\n", " '.',\n", " 'Yet',\n", " 'we',\n", " 'have',\n", " 'not',\n", " 'done',\n", " 'this',\n", " 'to',\n", " 'any',\n", " 'other',\n", " 'nation',\n", " '.',\n", " 'We',\n", " 'have',\n", " 'not',\n", " 'conquered',\n", " 'anyone',\n", " '.',\n", " 'We',\n", " 'have',\n", " 'not',\n", " 'grabbed',\n", " 'their',\n", " 'land',\n", " ',',\n", " 'their',\n", " 'culture',\n", " ',',\n", " 'their',\n", " 'history',\n", " 'and',\n", " 'tried',\n", " 'to',\n", " 'enforce',\n", " 'our',\n", " 'way',\n", " 'of',\n", " 'life',\n", " 'on',\n", " 'them',\n", " '.',\n", " 'Why',\n", " '?',\n", " 'Because',\n", " 'we',\n", " 'respect',\n", " 'the',\n", " 'freedom',\n", " 'of',\n", " 'others.That',\n", " 'is',\n", " 'why',\n", " 'my',\n", " 'first',\n", " 'vision',\n", " 'is',\n", " 'that',\n", " 'of',\n", " 'freedom',\n", " '.',\n", " 'I',\n", " 'believe',\n", " 'that',\n", " 'India',\n", " 'got',\n", " 'its',\n", " 'first',\n", " 'vision',\n", " 'of',\n", " 'this',\n", " 'in',\n", " '1857',\n", " ',',\n", " 'when',\n", " 'we',\n", " 'started',\n", " 'the',\n", " 'War',\n", " 'of',\n", " 'Independence',\n", " '.',\n", " 'It',\n", " 'is',\n", " 'this',\n", " 'freedom',\n", " 'that',\n", " 'we',\n", " 'must',\n", " 'protect',\n", " 'and',\n", " 'nurture',\n", " 'and',\n", " 'build',\n", " 'on',\n", " '.',\n", " 'If',\n", " 'we',\n", " 'are',\n", " 'not',\n", " 'free',\n", " ',',\n", " 'no',\n", " 'one',\n", " 'will',\n", " 'respect',\n", " 'us',\n", " '.',\n", " 'My',\n", " 'second',\n", " 'vision',\n", " 'for',\n", " 'India',\n", " '’',\n", " 's',\n", " 'development',\n", " '.',\n", " 'For',\n", " 'fifty',\n", " 'years',\n", " 'we',\n", " 'have',\n", " 'been',\n", " 'a',\n", " 'developing',\n", " 'nation',\n", " '.',\n", " 'It',\n", " 'is',\n", " 'time',\n", " 'we',\n", " 'see',\n", " 'ourselves',\n", " 'as',\n", " 'a',\n", " 'developed',\n", " 'nation',\n", " '.',\n", " 'We',\n", " 'are',\n", " 'among',\n", " 'the',\n", " 'top',\n", " '5',\n", " 'nations',\n", " 'of',\n", " 'the',\n", " 'world',\n", " 'in',\n", " 'terms',\n", " 'of',\n", " 'GDP',\n", " '.',\n", " 'We',\n", " 'have',\n", " 'a',\n", " '10',\n", " 'percent',\n", " 'growth',\n", " 'rate',\n", " 'in',\n", " 'most',\n", " 'areas',\n", " '.',\n", " 'Our',\n", " 'poverty',\n", " 'levels',\n", " 'are',\n", " 'falling',\n", " '.',\n", " 'Our',\n", " 'achievements',\n", " 'are',\n", " 'being',\n", " 'globally',\n", " 'recognised',\n", " 'today',\n", " '.',\n", " 'Yet',\n", " 'we',\n", " 'lack',\n", " 'the',\n", " 'self-confidence',\n", " 'to',\n", " 'see',\n", " 'ourselves',\n", " 'as',\n", " 'a',\n", " 'developed',\n", " 'nation',\n", " ',',\n", " 'self-reliant',\n", " 'and',\n", " 'self-assured',\n", " '.',\n", " 'Isn',\n", " '’',\n", " 't',\n", " 'this',\n", " 'incorrect',\n", " '?',\n", " 'I',\n", " 'have',\n", " 'a',\n", " 'third',\n", " 'vision',\n", " '.',\n", " 'India',\n", " 'must',\n", " 'stand',\n", " 'up',\n", " 'to',\n", " 'the',\n", " 'world',\n", " '.',\n", " 'Because',\n", " 'I',\n", " 'believe',\n", " 'that',\n", " 'unless',\n", " 'India',\n", " 'stands',\n", " 'up',\n", " 'to',\n", " 'the',\n", " 'world',\n", " ',',\n", " 'no',\n", " 'one',\n", " 'will',\n", " 'respect',\n", " 'us',\n", " '.',\n", " 'Only',\n", " 'strength',\n", " 'respects',\n", " 'strength',\n", " '.',\n", " 'We',\n", " 'must',\n", " 'be',\n", " 'strong',\n", " 'not',\n", " 'only',\n", " 'as',\n", " 'a',\n", " 'military',\n", " 'power',\n", " 'but',\n", " 'also',\n", " 'as',\n", " 'an',\n", " 'economic',\n", " 'power',\n", " '.',\n", " 'Both',\n", " 'must',\n", " 'go',\n", " 'hand-in-hand',\n", " '.',\n", " 'My',\n", " 'good',\n", " 'fortune',\n", " 'was',\n", " 'to',\n", " 'have',\n", " 'worked',\n", " 'with',\n", " 'three',\n", " 'great',\n", " 'minds',\n", " '.',\n", " 'Dr.',\n", " 'Vikram',\n", " 'Sarabhai',\n", " 'of',\n", " 'the',\n", " 'Dept',\n", " '.',\n", " 'of',\n", " 'space',\n", " ',',\n", " 'Professor',\n", " 'Satish',\n", " 'Dhawan',\n", " ',',\n", " 'who',\n", " 'succeeded',\n", " 'him',\n", " 'and',\n", " 'Dr.',\n", " 'Brahm',\n", " 'Prakash',\n", " ',',\n", " 'father',\n", " 'of',\n", " 'nuclear',\n", " 'material',\n", " '.',\n", " 'I',\n", " 'was',\n", " 'lucky',\n", " 'to',\n", " 'have',\n", " 'worked',\n", " 'with',\n", " 'all',\n", " 'three',\n", " 'of',\n", " 'them',\n", " 'closely',\n", " 'and',\n", " 'consider',\n", " 'this',\n", " 'the',\n", " 'great',\n", " 'opportunity',\n", " 'of',\n", " 'my',\n", " 'life',\n", " '.',\n", " 'I',\n", " 'see',\n", " 'four',\n", " 'milestones',\n", " 'in',\n", " 'my',\n", " 'career']" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "words" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HLJ28p011OTv" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "1_Tokenization_NLP.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 1 }