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Repository: dipanjanS/nlp_essentials
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Files: 8
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Directory structure:
gitextract_ngxsk4bi/
├── .gitignore
├── LICENSE
├── README.md
└── notebooks/
├── 01_Text_Wrangling_Examples.ipynb
├── 02_Text_Representation_Statistical_Models.ipynb
├── 03_Text_Representation_Embedding_Models.ipynb
├── 04_NLP_Applications_Text_Similarity_Content_Recommenders.ipynb
└── 05_NLP_Applications_Predicting_E_Commerce_Product_Recommendation_Ratings_from_Reviews_.ipynb
================================================
FILE CONTENTS
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FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
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*.so
# Distribution / packaging
.Python
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dist/
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var/
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share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
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htmlcov/
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coverage.xml
*.cover
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# Translations
*.mo
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# Django stuff:
*.log
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docs/_build/
# PyBuilder
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# Jupyter Notebook
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# IPython
profile_default/
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# pyenv
.python-version
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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__pypackages__/
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# Pyre type checker
.pyre/
================================================
FILE: LICENSE
================================================
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USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
================================================
FILE: README.md
================================================
# NLP Essentials
Essential and Fundametal aspects of Natural Language Processing with hands-on examples and case-studies
================================================
FILE: notebooks/01_Text_Wrangling_Examples.ipynb
================================================
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"accelerator": "GPU",
"anaconda-cloud": {},
"colab": {
"name": "01 - Text Wrangling Examples.ipynb",
"provenance": [],
"collapsed_sections": []
},
"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": "3.7.6"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "NtCf5TIaJpEr"
},
"source": [
"# Install Dependencies"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "ceSG71XiJoka",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 185
},
"outputId": "c1fa4f8d-9143-42bc-ef76-07166fc0710c"
},
"source": [
"import nltk\n",
"nltk.download('punkt')\n",
"nltk.download('wordnet')\n",
"nltk.download('stopwords')\n",
"nltk.download('averaged_perceptron_tagger')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n",
"[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/wordnet.zip.\n",
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n",
"[nltk_data] Downloading package averaged_perceptron_tagger to\n",
"[nltk_data] /root/nltk_data...\n",
"[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {
"tags": []
},
"execution_count": 1
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "AgeSwPsGJFWj"
},
"source": [
"# Case Conversion"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "OQp382lJJFWp",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "199a9b30-a3ae-414b-def0-4166e17b7ab8"
},
"source": [
"text = 'The quick brown fox jumped over The Big Dog'\n",
"text"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'The quick brown fox jumped over The Big Dog'"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "FaAwb7HZJFWz",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "e2c4bc72-3743-4176-a42b-9720354716b8"
},
"source": [
"text.lower()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'the quick brown fox jumped over the big dog'"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "ihX9LwVuJFW4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "34f136ca-0ea5-4531-e206-2c981ae6d6e3"
},
"source": [
"text.upper()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'THE QUICK BROWN FOX JUMPED OVER THE BIG DOG'"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "U24TBZ82JFW8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "e3fc2b71-b4f1-4ba4-93e9-7581c85d7566"
},
"source": [
"text.title()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'The Quick Brown Fox Jumped Over The Big Dog'"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "V3GzHq46JFW_"
},
"source": [
"# Tokenization"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "zIiPr5JBJFXA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "02958ece-7022-4a28-9591-d8caef757b87"
},
"source": [
"sample_text = (\"US unveils world's most powerful supercomputer, beats China. \" \n",
" \"The US has unveiled the world's most powerful supercomputer called 'Summit', \" \n",
" \"beating the previous record-holder China's Sunway TaihuLight. With a peak performance \"\n",
" \"of 200,000 trillion calculations per second, it is over twice as fast as Sunway TaihuLight, \"\n",
" \"which is capable of 93,000 trillion calculations per second. Summit has 4,608 servers, \"\n",
" \"which reportedly take up the size of two tennis courts.\")\n",
"sample_text"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"\"US unveils world's most powerful supercomputer, beats China. The US has unveiled the world's most powerful supercomputer called 'Summit', beating the previous record-holder China's Sunway TaihuLight. With a peak performance of 200,000 trillion calculations per second, it is over twice as fast as Sunway TaihuLight, which is capable of 93,000 trillion calculations per second. Summit has 4,608 servers, which reportedly take up the size of two tennis courts.\""
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "i2m8nEPmJFXD",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 84
},
"outputId": "83fe372f-8901-43d1-d6f5-fed5ee27cd91"
},
"source": [
"import nltk\n",
"\n",
"nltk.sent_tokenize(sample_text)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[\"US unveils world's most powerful supercomputer, beats China.\",\n",
" \"The US has unveiled the world's most powerful supercomputer called 'Summit', beating the previous record-holder China's Sunway TaihuLight.\",\n",
" 'With a peak performance of 200,000 trillion calculations per second, it is over twice as fast as Sunway TaihuLight, which is capable of 93,000 trillion calculations per second.',\n",
" 'Summit has 4,608 servers, which reportedly take up the size of two tennis courts.']"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "KjVNIwLoJFXG",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "093b33f0-6449-4fd0-c1ed-1dfc9bb6e318"
},
"source": [
"print(nltk.word_tokenize(sample_text))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"['US', 'unveils', 'world', \"'s\", 'most', 'powerful', 'supercomputer', ',', 'beats', 'China', '.', 'The', 'US', 'has', 'unveiled', 'the', 'world', \"'s\", 'most', 'powerful', 'supercomputer', 'called', \"'Summit\", \"'\", ',', 'beating', 'the', 'previous', 'record-holder', 'China', \"'s\", 'Sunway', 'TaihuLight', '.', 'With', 'a', 'peak', 'performance', 'of', '200,000', 'trillion', 'calculations', 'per', 'second', ',', 'it', 'is', 'over', 'twice', 'as', 'fast', 'as', 'Sunway', 'TaihuLight', ',', 'which', 'is', 'capable', 'of', '93,000', 'trillion', 'calculations', 'per', 'second', '.', 'Summit', 'has', '4,608', 'servers', ',', 'which', 'reportedly', 'take', 'up', 'the', 'size', 'of', 'two', 'tennis', 'courts', '.']\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "ZjhORAuPJFXL",
"colab": {}
},
"source": [
"import spacy\n",
"nlp = spacy.load('en')\n",
"\n",
"text_spacy = nlp(sample_text)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "DR6LA_YHJFXN",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 84
},
"outputId": "630d9f19-8658-4b3b-bbd8-069fb3594601"
},
"source": [
"[obj.text for obj in text_spacy.sents]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[\"US unveils world's most powerful supercomputer, beats China.\",\n",
" \"The US has unveiled the world's most powerful supercomputer called 'Summit', beating the previous record-holder China's Sunway TaihuLight.\",\n",
" 'With a peak performance of 200,000 trillion calculations per second, it is over twice as fast as Sunway TaihuLight, which is capable of 93,000 trillion calculations per second.',\n",
" 'Summit has 4,608 servers, which reportedly take up the size of two tennis courts.']"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "DBuAHdR8JFXQ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "f6acbbf6-e500-42c4-f06d-f1f8942ec548"
},
"source": [
"print([obj.text for obj in text_spacy])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"['US', 'unveils', 'world', \"'s\", 'most', 'powerful', 'supercomputer', ',', 'beats', 'China', '.', 'The', 'US', 'has', 'unveiled', 'the', 'world', \"'s\", 'most', 'powerful', 'supercomputer', 'called', \"'\", 'Summit', \"'\", ',', 'beating', 'the', 'previous', 'record', '-', 'holder', 'China', \"'s\", 'Sunway', 'TaihuLight', '.', 'With', 'a', 'peak', 'performance', 'of', '200,000', 'trillion', 'calculations', 'per', 'second', ',', 'it', 'is', 'over', 'twice', 'as', 'fast', 'as', 'Sunway', 'TaihuLight', ',', 'which', 'is', 'capable', 'of', '93,000', 'trillion', 'calculations', 'per', 'second', '.', 'Summit', 'has', '4,608', 'servers', ',', 'which', 'reportedly', 'take', 'up', 'the', 'size', 'of', 'two', 'tennis', 'courts', '.']\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fhxnJkIsJFXS"
},
"source": [
"# Removing HTML tags & noise"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "E3qV1WOpJFXT",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 470
},
"outputId": "82fbdc9e-df22-410d-bd33-1096b34647e6"
},
"source": [
"import requests\n",
"\n",
"data = requests.get('http://www.gutenberg.org/cache/epub/8001/pg8001.html')\n",
"content = data.text\n",
"print(content[2745:3948])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"<p id=\"id00011\" style=\"margin-top: 2em\">*** START OF THE PROJECT GUTENBERG EBOOK, THE BIBLE, KING JAMES, BOOK 1***</p>\r\n",
"\r\n",
"<p id=\"id00012\" style=\"margin-top: 4em\">This eBook was produced by David Widger\r\n",
"with the help of Derek Andrew's text from January 1992\r\n",
"and the work of Bryan Taylor in November 2002.</p>\r\n",
"\r\n",
"<h1 id=\"id00013\" style=\"margin-top: 5em\">Book 01 Genesis</h1>\r\n",
"\r\n",
"<p id=\"id00014\">01:001:001 In the beginning God created the heaven and the earth.</p>\r\n",
"\r\n",
"<p id=\"id00015\" style=\"margin-left: 0%; margin-right: 0%\">01:001:002 And the earth was without form, and void; and darkness was\r\n",
" upon the face of the deep. And the Spirit of God moved upon\r\n",
" the face of the waters.</p>\r\n",
"\r\n",
"<p id=\"id00016\">01:001:003 And God said, Let there be light: and there was light.</p>\r\n",
"\r\n",
"<p id=\"id00017\">01:001:004 And God saw the light, that it was good: and God divided the<br/>\r\n",
"\r\n",
" light from the darkness.<br/>\r\n",
"</p>\r\n",
"\r\n",
"<p id=\"id00018\">01:001:005 And God called the light Day, and the darkness he called<br/>\r\n",
"\r\n",
" Night. And the evening and the morning were the first day.<br/>\r\n",
"</p>\r\n",
"\r\n",
"<p id=\"id00019\">01:001:006 And God said, Let there be a firmament in the mi\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "E6UAz3mjJFXY",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269
},
"outputId": "0549c5a6-1814-4e82-8046-d32b40647d1f"
},
"source": [
"import re\n",
"from bs4 import BeautifulSoup\n",
"\n",
"def strip_html_tags(text):\n",
" soup = BeautifulSoup(text, \"html.parser\")\n",
" [s.extract() for s in soup(['iframe', 'script'])]\n",
" stripped_text = soup.get_text()\n",
" stripped_text = re.sub(r'[\\r|\\n|\\r\\n]+', '\\n', stripped_text)\n",
" return stripped_text\n",
"\n",
"clean_content = strip_html_tags(content)\n",
"print(clean_content[1163:1957])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"*** START OF THE PROJECT GUTENBERG EBOOK, THE BIBLE, KING JAMES, BOOK 1***\n",
"This eBook was produced by David Widger\n",
"with the help of Derek Andrew's text from January 1992\n",
"and the work of Bryan Taylor in November 2002.\n",
"Book 01 Genesis\n",
"01:001:001 In the beginning God created the heaven and the earth.\n",
"01:001:002 And the earth was without form, and void; and darkness was\n",
" upon the face of the deep. And the Spirit of God moved upon\n",
" the face of the waters.\n",
"01:001:003 And God said, Let there be light: and there was light.\n",
"01:001:004 And God saw the light, that it was good: and God divided the\n",
" light from the darkness.\n",
"01:001:005 And God called the light Day, and the darkness he called\n",
" Night. And the evening and the morning were the first day.\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9fJi5YyKJFXc"
},
"source": [
"# Removing Accented Characters"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Ps9wmhv9JFXd",
"colab": {}
},
"source": [
"import unicodedata\n",
"\n",
"def remove_accented_chars(text):\n",
" text = unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n",
" return text"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Mc7JR8CQJFXh",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "392b46f9-4945-47e0-b942-9d50de2fb3ff"
},
"source": [
"s = 'Sómě Áccěntěd těxt'\n",
"s"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Sómě Áccěntěd těxt'"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "I6a-e-mVJFXm",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "9073d7d9-198f-4c5c-9de9-2539cda1adcd"
},
"source": [
"remove_accented_chars(s)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Some Accented text'"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gj8CyGmPJFXr"
},
"source": [
"# Removing Special Characters, Numbers and Symbols"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "1dkc4ESDJFXs",
"colab": {}
},
"source": [
"import re\n",
"\n",
"def remove_special_characters(text, remove_digits=False):\n",
" pattern = r'[^a-zA-Z0-9\\s]' if not remove_digits else r'[^a-zA-Z\\s]'\n",
" text = re.sub(pattern, '', text)\n",
" return text\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "XUwKvQ-1JFXx",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "deb1477f-9de1-44fc-801b-f80d0930624f"
},
"source": [
"s = \"Well this was fun! See you at 7:30, What do you think!!? #$@@9318@ 🙂🙂🙂\"\n",
"s"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Well this was fun! See you at 7:30, What do you think!!? #$@@9318@ 🙂🙂🙂'"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Sy9x4XFyJFYL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "01596e65-1935-4095-b91a-1cd14da1ef48"
},
"source": [
"remove_special_characters(s, remove_digits=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Well this was fun See you at What do you think '"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "s2vT0GK5JFYQ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "e8763d3d-6f64-4308-f39d-8487a97ffcf2"
},
"source": [
"remove_special_characters(s)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Well this was fun See you at 730 What do you think 9318 '"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ho6h68QbJFYX"
},
"source": [
"# Expanding Contractions"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "mgGTT1URJFYY",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 353
},
"outputId": "2aa9f59a-9d7a-4436-9f90-0ba12a1b6016"
},
"source": [
"!pip install contractions\n",
"!pip install textsearch"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting contractions\n",
" Downloading https://files.pythonhosted.org/packages/85/41/c3dfd5feb91a8d587ed1a59f553f07c05f95ad4e5d00ab78702fbf8fe48a/contractions-0.0.24-py2.py3-none-any.whl\n",
"Collecting textsearch\n",
" Downloading https://files.pythonhosted.org/packages/42/a8/03407021f9555043de5492a2bd7a35c56cc03c2510092b5ec018cae1bbf1/textsearch-0.0.17-py2.py3-none-any.whl\n",
"Collecting pyahocorasick\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/f4/9f/f0d8e8850e12829eea2e778f1c90e3c53a9a799b7f412082a5d21cd19ae1/pyahocorasick-1.4.0.tar.gz (312kB)\n",
"\u001b[K |████████████████████████████████| 317kB 3.8MB/s \n",
"\u001b[?25hCollecting Unidecode\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/d0/42/d9edfed04228bacea2d824904cae367ee9efd05e6cce7ceaaedd0b0ad964/Unidecode-1.1.1-py2.py3-none-any.whl (238kB)\n",
"\u001b[K |████████████████████████████████| 245kB 15.6MB/s \n",
"\u001b[?25hBuilding wheels for collected packages: pyahocorasick\n",
" Building wheel for pyahocorasick (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pyahocorasick: filename=pyahocorasick-1.4.0-cp36-cp36m-linux_x86_64.whl size=81699 sha256=b766a035314d66115551991c1220d24c4a42480cc3e1f4b7ae56a50ba5e9f62a\n",
" Stored in directory: /root/.cache/pip/wheels/0a/90/61/87a55f5b459792fbb2b7ba6b31721b06ff5cf6bde541b40994\n",
"Successfully built pyahocorasick\n",
"Installing collected packages: pyahocorasick, Unidecode, textsearch, contractions\n",
"Successfully installed Unidecode-1.1.1 contractions-0.0.24 pyahocorasick-1.4.0 textsearch-0.0.17\n",
"Requirement already satisfied: textsearch in /usr/local/lib/python3.6/dist-packages (0.0.17)\n",
"Requirement already satisfied: Unidecode in /usr/local/lib/python3.6/dist-packages (from textsearch) (1.1.1)\n",
"Requirement already satisfied: pyahocorasick in /usr/local/lib/python3.6/dist-packages (from textsearch) (1.4.0)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "5xWsgO-jJFYc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "b02a9709-8e7e-4174-d46c-769d73aeadda"
},
"source": [
"s = \"Y'all can't expand contractions I'd think! You wouldn't be able to. How'd you do it?\"\n",
"s"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"\"Y'all can't expand contractions I'd think! You wouldn't be able to. How'd you do it?\""
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "S2QTF2HFJFYi",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 185
},
"outputId": "d01e87fb-cc90-4f97-e01c-f8a76d2e1c60"
},
"source": [
"import contractions\n",
"\n",
"list(contractions.contractions_dict.items())[:10]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[(\"ain't\", 'are not'),\n",
" (\"aren't\", 'are not'),\n",
" (\"can't\", 'can not'),\n",
" (\"can't've\", 'can not have'),\n",
" (\"'cause\", 'because'),\n",
" (\"could've\", 'could have'),\n",
" (\"couldn't\", 'could not'),\n",
" (\"couldn't've\", 'could not have'),\n",
" (\"didn't\", 'did not'),\n",
" (\"doesn't\", 'does not')]"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "KoIGJXqCJFYo",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "1cae6a7b-d520-46eb-ce81-ec50089f7ab3"
},
"source": [
"contractions.fix(s)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'you all can not expand contractions I would think! You would not be able to. how did you do it?'"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "EeUHPmhDJFZC"
},
"source": [
"# Stemming"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "8ndJ4XOKJFZD",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "fb53058e-bbdf-489b-f57d-fbdb7e7395c7"
},
"source": [
"# Porter Stemmer\n",
"from nltk.stem import PorterStemmer\n",
"ps = PorterStemmer()\n",
"\n",
"ps.stem('jumping'), ps.stem('jumps'), ps.stem('jumped')"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('jump', 'jump', 'jump')"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "CmWLISH-JFZG",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "4e8dcb51-121a-4f3b-8522-a7db0cf81357"
},
"source": [
"ps.stem('lying')"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'lie'"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Q7KRj1jtJFZJ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "a1944d27-3e35-4a4e-dc10-41f0b15384bc"
},
"source": [
"ps.stem('strange')"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'strang'"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "cQNUmpfLJFZu"
},
"source": [
"# Lemmatization"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "16ygP7t1JFZv",
"colab": {}
},
"source": [
"from nltk.stem import WordNetLemmatizer\n",
"wnl = WordNetLemmatizer()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "AieUIjYaJFZ3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 84
},
"outputId": "09dc4df1-9ff9-48d4-f11a-cf63cbf6f3d9"
},
"source": [
"help(wnl.lemmatize)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Help on method lemmatize in module nltk.stem.wordnet:\n",
"\n",
"lemmatize(word, pos='n') method of nltk.stem.wordnet.WordNetLemmatizer instance\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "_ZPcwz44JFZ7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 50
},
"outputId": "c0b18e8d-d034-4241-fac2-53ff4ef56d1c"
},
"source": [
"# lemmatize nouns\n",
"print(wnl.lemmatize('cars', 'n'))\n",
"print(wnl.lemmatize('boxes', 'n'))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"car\n",
"box\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "KJN-uQ28JFZ_",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 50
},
"outputId": "49826ee7-990d-44ee-c0d2-09b58e10fdb4"
},
"source": [
"# lemmatize verbs\n",
"print(wnl.lemmatize('running', 'v'))\n",
"print(wnl.lemmatize('ate', 'v'))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"run\n",
"eat\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "L0u5uZeoJFaF",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 50
},
"outputId": "f7cc036f-5b61-4b0d-9ed2-7af8391851b9"
},
"source": [
"# lemmatize adjectives\n",
"print(wnl.lemmatize('saddest', 'a'))\n",
"print(wnl.lemmatize('fancier', 'a'))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"sad\n",
"fancy\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "NhKXkdckJFaN",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 67
},
"outputId": "cd37a4a9-c4b2-48a5-a486-869ad8b8fea1"
},
"source": [
"# ineffective lemmatization\n",
"print(wnl.lemmatize('ate', 'n'))\n",
"print(wnl.lemmatize('fancier', 'v'))\n",
"print(wnl.lemmatize('fancier'))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"ate\n",
"fancier\n",
"fancier\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Z4g85bOGJFaQ",
"colab": {}
},
"source": [
"s = 'The brown foxes are quick and they are jumping over the sleeping lazy dogs!'"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "NQ1S2ngz7B84",
"colab_type": "text"
},
"source": [
"### Tokenize"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "0l372SiEJFaU",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "cd2a469c-26ed-4d06-cce1-91b6e29650af"
},
"source": [
"tokens = nltk.word_tokenize(s)\n",
"print(tokens)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"['The', 'brown', 'foxes', 'are', 'quick', 'and', 'they', 'are', 'jumping', 'over', 'the', 'sleeping', 'lazy', 'dogs', '!']\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "s1FHAghFJFaX",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "765ad93e-2a6a-4666-c649-3c529d8cdf95"
},
"source": [
"lemmatized_text = ' '.join(wnl.lemmatize(token) for token in tokens)\n",
"lemmatized_text"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'The brown fox are quick and they are jumping over the sleeping lazy dog !'"
]
},
"metadata": {
"tags": []
},
"execution_count": 44
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "d0-fgmbi7E5_",
"colab_type": "text"
},
"source": [
"### POS Tagging"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "UDffFU3gJFaZ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "fcfbd8a2-dc38-4578-c479-b4672b58cde3"
},
"source": [
"tagged_tokens = nltk.pos_tag(tokens)\n",
"print(tagged_tokens)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"[('The', 'DT'), ('brown', 'JJ'), ('foxes', 'NNS'), ('are', 'VBP'), ('quick', 'JJ'), ('and', 'CC'), ('they', 'PRP'), ('are', 'VBP'), ('jumping', 'VBG'), ('over', 'IN'), ('the', 'DT'), ('sleeping', 'VBG'), ('lazy', 'JJ'), ('dogs', 'NNS'), ('!', '.')]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9STnRHVt7HRG",
"colab_type": "text"
},
"source": [
"### Tag conversion to WordNet Tags"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "2S9kS_xPJFaf",
"colab": {}
},
"source": [
"from nltk.corpus import wordnet\n",
"\n",
"def pos_tag_wordnet(tagged_tokens):\n",
" tag_map = {'j': wordnet.ADJ, 'v': wordnet.VERB, 'n': wordnet.NOUN, 'r': wordnet.ADV}\n",
" new_tagged_tokens = [(word, tag_map.get(tag[0].lower(), wordnet.NOUN))\n",
" for word, tag in tagged_tokens]\n",
" return new_tagged_tokens"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "TbijTK6YJFaj",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "c97e38b3-8b0f-43ee-848b-bbbf9449ebfb"
},
"source": [
"wordnet_tokens = pos_tag_wordnet(tagged_tokens)\n",
"print(wordnet_tokens)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"[('The', 'n'), ('brown', 'a'), ('foxes', 'n'), ('are', 'v'), ('quick', 'a'), ('and', 'n'), ('they', 'n'), ('are', 'v'), ('jumping', 'v'), ('over', 'n'), ('the', 'n'), ('sleeping', 'v'), ('lazy', 'a'), ('dogs', 'n'), ('!', 'n')]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qKia_-ov7KLH",
"colab_type": "text"
},
"source": [
"### Effective Lemmatization"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "tNOpTLDTJFal",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "575e08b1-a48d-40a8-abde-363fd671fa93"
},
"source": [
"lemmatized_text = ' '.join(wnl.lemmatize(word, tag) for word, tag in wordnet_tokens)\n",
"lemmatized_text"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'The brown fox be quick and they be jump over the sleep lazy dog !'"
]
},
"metadata": {
"tags": []
},
"execution_count": 50
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9zisZIs1JFan"
},
"source": [
"### Your turn: Define a function such that you put all the above steps together so that it does the following\n",
"\n",
"- Function name is __`wordnet_lemmatize_text(...)`__\n",
"- Input is a variable __`text`__ which should take in a document (bunch of words)\n",
"- Call the earlier defined functions and utilize them\n",
"- Return lemmatized text as the output (as a string)"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "I6LditBNJFao",
"colab": {}
},
"source": [
"wnl = WordNetLemmatizer()\n",
"\n",
"def wordnet_lemmatize_text(text):\n",
" tagged_tokens = nltk.pos_tag(nltk.word_tokenize(text))\n",
" wordnet_tokens = pos_tag_wordnet(tagged_tokens)\n",
" lemmatized_text = ' '.join(wnl.lemmatize(word, tag) for word, tag in wordnet_tokens)\n",
" return lemmatized_text"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "UWQeSVxGJFap"
},
"source": [
"### Your Turn: Now call the function on the below sentence and test it"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "FUvJQk-eJFaq",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "41276cb8-3c6e-4382-9f25-2516899052e2"
},
"source": [
"s"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'The brown foxes are quick and they are jumping over the sleeping lazy dogs!'"
]
},
"metadata": {
"tags": []
},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "7xE0WwbJJFas",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "638b66b3-02b5-41ff-ec4e-6a2deff153d9"
},
"source": [
"wordnet_lemmatize_text(s)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'The brown fox be quick and they be jump over the sleep lazy dog !'"
]
},
"metadata": {
"tags": []
},
"execution_count": 53
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KgQJp2SH7OC_",
"colab_type": "text"
},
"source": [
"## Lemmatization with Spacy"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "3N2ExlFqJFaw",
"colab": {}
},
"source": [
"import spacy\n",
"nlp = spacy.load('en', parse=False, tag=False, entity=False)\n",
"\n",
"def spacy_lemmatize_text(text):\n",
" text = nlp(text)\n",
" text = ' '.join([word.lemma_ if word.lemma_ != '-PRON-' else word.text for word in text])\n",
" return text"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "ga-E47JKJFaz",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "76333567-ae35-46ee-fdf9-260b3926cc31"
},
"source": [
"s"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'The brown foxes are quick and they are jumping over the sleeping lazy dogs!'"
]
},
"metadata": {
"tags": []
},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Bb-PrIeqJFa5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "b5b00364-7d85-4240-cf55-21f8fef8fa5d"
},
"source": [
"spacy_lemmatize_text(s)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'the brown fox be quick and they be jump over the sleep lazy dog !'"
]
},
"metadata": {
"tags": []
},
"execution_count": 56
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "aQsKAXlvJFa7"
},
"source": [
"# Stopword Removal"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "VkJLKKxrJFa7",
"colab": {}
},
"source": [
"def remove_stopwords(text, is_lower_case=False, stopwords=None):\n",
" if not stopwords:\n",
" stopwords = nltk.corpus.stopwords.words('english')\n",
" tokens = nltk.word_tokenize(text)\n",
" tokens = [token.strip() for token in tokens]\n",
" \n",
" if is_lower_case:\n",
" filtered_tokens = [token for token in tokens if token not in stopwords]\n",
" else:\n",
" filtered_tokens = [token for token in tokens if token.lower() not in stopwords]\n",
" \n",
" filtered_text = ' '.join(filtered_tokens) \n",
" return filtered_text"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "veJLEhzKJFa-",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "3272c105-0664-4349-8f42-637cadd56727"
},
"source": [
"stop_words = nltk.corpus.stopwords.words('english')\n",
"print(stop_words[:10])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\"]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "ycusSsPBJFbA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "186389a0-7131-45eb-a876-6dde01d91fe9"
},
"source": [
"s"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'The brown foxes are quick and they are jumping over the sleeping lazy dogs!'"
]
},
"metadata": {
"tags": []
},
"execution_count": 59
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "oWKjTPnzJFbD",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "16fe7d8e-8248-4415-9ec8-902bd718295c"
},
"source": [
"remove_stopwords(s, is_lower_case=False)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'brown foxes quick jumping sleeping lazy dogs !'"
]
},
"metadata": {
"tags": []
},
"execution_count": 60
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4bcnWSnAJFbG"
},
"source": [
"### Your turn: Remove the words 'the' and 'brown' from the stop_words list and call the function with this new list"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "rPAM2rNZJFbH",
"colab": {}
},
"source": [
"stop_words.remove('the')\n",
"stop_words.append('brown')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "qk2Y-nbZJFbJ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "a433ccad-6e6a-4671-b4b7-9723f45153af"
},
"source": [
"remove_stopwords(s, is_lower_case=False, stopwords=stop_words)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'The foxes quick jumping the sleeping lazy dogs !'"
]
},
"metadata": {
"tags": []
},
"execution_count": 62
}
]
}
]
}
================================================
FILE: notebooks/02_Text_Representation_Statistical_Models.ipynb
================================================
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "02-Text Representation - Statistical Models.ipynb",
"provenance": [],
"collapsed_sections": []
},
"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": "3.7.6"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "-dWlnZ1vRP6H"
},
"source": [
"# Text Representation with Feature Engineering\n",
"\n",
"### Exploring Traditional Statistical Models\n",
"\n",
"Feature Engineering is often known as the secret sauce to creating superior and better performing machine learning models. Just one excellent feature could be your ticket to winning a Kaggle challenge! The importance of feature engineering is even more important for unstructured, textual data because we need to convert free flowing text into some numeric representations which can then be understood by machine learning algorithms. \n",
"\n",
"Here we will explore the following feature engineering techniques:\n",
"\n",
"- Bag of Words Model (TF)\n",
"- Bag of N-grams Model\n",
"- TF-IDF Model\n",
"- Similarity Features"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "NYC_RfbeRP6J"
},
"source": [
"# Prepare a Sample Corpus\n",
"\n",
"Let’s now take a sample corpus of documents on which we will run most of our analyses in this article. A corpus is typically a collection of text documents usually belonging to one or more subjects or domains."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "6xn8eDqARP6J",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284
},
"outputId": "8ee06c2b-ddf7-4e5a-a23c-9b8038d3d9b2"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"pd.options.display.max_colwidth = 200\n",
"\n",
"corpus = ['The sky is blue and beautiful.',\n",
" 'Love this blue and beautiful sky!',\n",
" 'The quick brown fox jumps over the lazy dog.',\n",
" \"A king's breakfast has sausages, ham, bacon, eggs, toast and beans\",\n",
" 'I love green eggs, ham, sausages and bacon!',\n",
" 'The brown fox is quick and the blue dog is lazy!',\n",
" 'The sky is very blue and the sky is very beautiful today',\n",
" 'The dog is lazy but the brown fox is quick!' \n",
"]\n",
"labels = ['weather', 'weather', 'animals', 'food', 'food', 'animals', 'weather', 'animals']\n",
"\n",
"corpus = np.array(corpus)\n",
"corpus_df = pd.DataFrame({'Document': corpus, \n",
" 'Category': labels})\n",
"corpus_df = corpus_df[['Document', 'Category']]\n",
"corpus_df"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Document</th>\n",
" <th>Category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>The sky is blue and beautiful.</td>\n",
" <td>weather</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Love this blue and beautiful sky!</td>\n",
" <td>weather</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>The quick brown fox jumps over the lazy dog.</td>\n",
" <td>animals</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A king's breakfast has sausages, ham, bacon, eggs, toast and beans</td>\n",
" <td>food</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>I love green eggs, ham, sausages and bacon!</td>\n",
" <td>food</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>The brown fox is quick and the blue dog is lazy!</td>\n",
" <td>animals</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>The sky is very blue and the sky is very beautiful today</td>\n",
" <td>weather</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>The dog is lazy but the brown fox is quick!</td>\n",
" <td>animals</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Document Category\n",
"0 The sky is blue and beautiful. weather\n",
"1 Love this blue and beautiful sky! weather\n",
"2 The quick brown fox jumps over the lazy dog. animals\n",
"3 A king's breakfast has sausages, ham, bacon, eggs, toast and beans food\n",
"4 I love green eggs, ham, sausages and bacon! food\n",
"5 The brown fox is quick and the blue dog is lazy! animals\n",
"6 The sky is very blue and the sky is very beautiful today weather\n",
"7 The dog is lazy but the brown fox is quick! animals"
]
},
"metadata": {
"tags": []
},
"execution_count": 1
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "QOMGTkvCRP6N"
},
"source": [
"You can see that we have taken a few sample text documents belonging to different categories for our toy corpus. Before we talk about feature engineering, as always, we need to do some data pre-processing or wrangling to remove unnecessary characters, symbols and tokens."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "nAcT9NHkRP6O"
},
"source": [
"# Simple Text Pre-processing\n",
"\n",
"Since the focus of this unit is on feature engineering, we will build a simple text pre-processor which focuses on removing special characters, extra whitespaces, digits, stopwords and lower casing the text corpus."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "vSIxl_PUSCvj",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 101
},
"outputId": "6477f365-d714-461e-bac4-cf4bc17705f5"
},
"source": [
"import nltk\n",
"nltk.download('stopwords')\n",
"nltk.download('punkt')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n",
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "OElcyG9WRP6P",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 118
},
"outputId": "fcfae9fa-349d-4e77-9f75-e5f241dfe5d9"
},
"source": [
"import nltk\n",
"import re\n",
"\n",
"stop_words = nltk.corpus.stopwords.words('english')\n",
"\n",
"def normalize_document(doc):\n",
" # lower case and remove special characters\\whitespaces\n",
" doc = re.sub(r'[^a-zA-Z\\s]', '', doc, re.I|re.A)\n",
" doc = doc.lower()\n",
" doc = doc.strip()\n",
" # tokenize document\n",
" tokens = nltk.word_tokenize(doc)\n",
" # filter stopwords out of document\n",
" filtered_tokens = [token for token in tokens if token not in stop_words]\n",
" # re-create document from filtered tokens\n",
" doc = ' '.join(filtered_tokens)\n",
" return doc\n",
"\n",
"normalize_corpus = np.vectorize(normalize_document)\n",
"\n",
"norm_corpus = normalize_corpus(corpus)\n",
"norm_corpus"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['sky blue beautiful', 'love blue beautiful sky',\n",
" 'quick brown fox jumps lazy dog',\n",
" 'kings breakfast sausages ham bacon eggs toast beans',\n",
" 'love green eggs ham sausages bacon',\n",
" 'brown fox quick blue dog lazy', 'sky blue sky beautiful today',\n",
" 'dog lazy brown fox quick'], dtype='<U51')"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "MMBWGlKTRP6T"
},
"source": [
"# Bag of Words Model - TF\n",
"\n",
"This is perhaps the most simple vector space representational model for unstructured text. A vector space model is simply a mathematical model to represent unstructured text (or any other data) as numeric vectors, such that each dimension of the vector is a specific feature\\attribute. The bag of words model represents each text document as a numeric vector where each dimension is a specific word from the corpus and the value could be its frequency in the document, occurrence (denoted by 1 or 0) or even weighted values. The model’s name is such because each document is represented literally as a ‘bag’ of its own words, disregarding word orders, sequences and grammar."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "d7mAqxTlRP6U",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 151
},
"outputId": "f771b092-0d12-48c5-9182-5beb92643376"
},
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"cv = CountVectorizer(min_df=0., max_df=1.)\n",
"cv_matrix = cv.fit_transform(norm_corpus)\n",
"cv_matrix = cv_matrix.toarray()\n",
"cv_matrix"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
" [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0],\n",
" [0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0],\n",
" [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0],\n",
" [1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0],\n",
" [0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0],\n",
" [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1],\n",
" [0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "lPp6Bzs7RP6W"
},
"source": [
"Thus you can see that our documents have been converted into numeric vectors such that each document is represented by one vector (row) in the above feature matrix. The following code will help represent this in a more easy to understand format."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "FVsdSiwARP6W",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284
},
"outputId": "03bf4798-1714-41d8-fc3c-6a93d003c393"
},
"source": [
"# get all unique words in the corpus\n",
"vocab = cv.get_feature_names()\n",
"# show document feature vectors\n",
"pd.DataFrame(cv_matrix, columns=vocab)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bacon</th>\n",
" <th>beans</th>\n",
" <th>beautiful</th>\n",
" <th>blue</th>\n",
" <th>breakfast</th>\n",
" <th>brown</th>\n",
" <th>dog</th>\n",
" <th>eggs</th>\n",
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" <th>green</th>\n",
" <th>ham</th>\n",
" <th>jumps</th>\n",
" <th>kings</th>\n",
" <th>lazy</th>\n",
" <th>love</th>\n",
" <th>quick</th>\n",
" <th>sausages</th>\n",
" <th>sky</th>\n",
" <th>toast</th>\n",
" <th>today</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "A_1k60ZERP6Z"
},
"source": [
"This should make things more clearer! You can clearly see that each column or dimension in the feature vectors represents a word from the corpus and each row represents one of our documents. The value in any cell, represents the number of times that word (represented by column) occurs in the specific document (represented by row). Hence if a corpus of documents consists of N unique words across all the documents, we would have an N-dimensional vector for each of the documents."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "0DfPJwSsRP6Z"
},
"source": [
"# Bag of N-Grams Model\n",
"\n",
"A word is just a single token, often known as a unigram or 1-gram. We already know that the Bag of Words model doesn’t consider order of words. But what if we also wanted to take into account phrases or collection of words which occur in a sequence? N-grams help us achieve that. An N-gram is basically a collection of word tokens from a text document such that these tokens are contiguous and occur in a sequence. Bi-grams indicate n-grams of order 2 (two words), Tri-grams indicate n-grams of order 3 (three words), and so on. The Bag of N-Grams model is hence just an extension of the Bag of Words model so we can also leverage N-gram based features. The following example depicts bi-gram based features in each document feature vector."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "4n1N32JyRP6a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 321
},
"outputId": "5c091718-a76c-448c-add9-311f32486230"
},
"source": [
"# you can set the n-gram range to 1,2 to get unigrams as well as bigrams\n",
"bv = CountVectorizer(ngram_range=(2,2))\n",
"bv_matrix = bv.fit_transform(norm_corpus)\n",
"\n",
"bv_matrix = bv_matrix.toarray()\n",
"vocab = bv.get_feature_names()\n",
"pd.DataFrame(bv_matrix, columns=vocab)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"text/plain": [
" bacon eggs beautiful sky ... sky blue toast beans\n",
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},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "bW6F4h8QRP6c"
},
"source": [
"This gives us feature vectors for our documents, where each feature consists of a bi-gram representing a sequence of two words and values represent how many times the bi-gram was present for our documents."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "pPwKOnnkRP6d"
},
"source": [
"# TF-IDF Model\n",
"\n",
"There are some potential problems which might arise with the Bag of Words model when it is used on large corpora. Since the feature vectors are based on absolute term frequencies, there might be some terms which occur frequently across all documents and these may tend to overshadow other terms in the feature set. The TF-IDF model tries to combat this issue by using a scaling or normalizing factor in its computation. ___TF-IDF___ stands for __Term Frequency-Inverse Document Frequency__, which uses a combination of two metrics in its computation, namely: ___term frequency (tf)___ and ___inverse document frequency (idf)___. This technique was developed for ranking results for queries in search engines and now it is an indispensable model in the world of information retrieval and NLP.\n",
"\n",
"Mathematically, we can define TF-IDF as ___tfidf = tf x idf___, which can be expanded further to be represented as follows.\n",
"\n",
"\n",
"\n",
"Here, ___tfidf(w, D)___ is the TF-IDF score for word __w__ in document __D__. \n",
"- The term ___tf(w, D)___ represents the term frequency of the word __w__ in document __D__, which can be obtained from the Bag of Words model. \n",
"- The term ___idf(w, D)___ is the inverse document frequency for the term __w__, which can be computed as the log transform of the total number of documents in the corpus __C__ divided by the document frequency of the word __w__, which is basically the frequency of documents in the corpus where the word __w__ occurs. \n",
"\n",
"There are multiple variants of this model but they all end up giving quite similar results. Let’s apply this on our corpus now!"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "sFp9zDbgRP6d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284
},
"outputId": "009f142f-1b99-4bfd-9e78-2bbf9db03807"
},
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"tv = TfidfVectorizer(min_df=0., max_df=1., use_idf=True)\n",
"tv_matrix = tv.fit_transform(norm_corpus)\n",
"tv_matrix = tv_matrix.toarray()\n",
"\n",
"vocab = tv.get_feature_names()\n",
"pd.DataFrame(np.round(tv_matrix, 2), columns=vocab)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
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" <td>0.42</td>\n",
" <td>0.42</td>\n",
" <td>0.00</td>\n",
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" <td>0.00</td>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.36</td>\n",
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" <td>0.45</td>\n",
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" <td>0.45</td>\n",
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" <td>0.00</td>\n",
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" <td>0.00</td>\n",
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"</table>\n",
"</div>"
],
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" bacon beans beautiful blue ... sausages sky toast today\n",
"0 0.00 0.00 0.60 0.53 ... 0.00 0.60 0.00 0.0\n",
"1 0.00 0.00 0.49 0.43 ... 0.00 0.49 0.00 0.0\n",
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"3 0.32 0.38 0.00 0.00 ... 0.32 0.00 0.38 0.0\n",
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"7 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 0.0\n",
"\n",
"[8 rows x 20 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "0jvKPWIwRP6g"
},
"source": [
"The TF-IDF based feature vectors for each of our text documents show scaled and normalized values as compared to the raw Bag of Words model values. "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7xzqFWAkRP6h"
},
"source": [
"# Document Similarity\n",
"\n",
"Document similarity is the process of using a distance or similarity based metric that can be used to identify how similar a text document is with any other document(s) based on features extracted from the documents like bag of words or tf-idf.\n",
"\n",
"Thus you can see that we can build on top of the tf-idf based features we engineered in the previous section and use them to generate new features which can be useful in domains like search engines, document clustering and information retrieval by leveraging these similarity based features.\n",
"\n",
"Pairwise document similarity in a corpus involves computing document similarity for each pair of documents in a corpus. Thus if you have C documents in a corpus, you would end up with a C x C matrix such that each row and column represents the similarity score for a pair of documents, which represent the indices at the row and column, respectively. There are several similarity and distance metrics that are used to compute document similarity. These include cosine distance/similarity, euclidean distance, manhattan distance, BM25 similarity, jaccard distance and so on. In our analysis, we will be using perhaps the most popular and widely used similarity metric,\n",
"cosine similarity and compare pairwise document similarity based on their TF-IDF feature vectors."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "zyToCxp9RP6i",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284
},
"outputId": "f9fa6f92-dded-4155-86fe-e6ebc8a5e8a7"
},
"source": [
"from sklearn.metrics.pairwise import cosine_similarity\n",
"\n",
"similarity_matrix = cosine_similarity(tv_matrix)\n",
"similarity_df = pd.DataFrame(similarity_matrix)\n",
"similarity_df"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
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" <td>0.192353</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>0.817246</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 ... 5 6 7\n",
"0 1.000000 0.820599 0.000000 ... 0.192353 0.817246 0.000000\n",
"1 0.820599 1.000000 0.000000 ... 0.157845 0.670631 0.000000\n",
"2 0.000000 0.000000 1.000000 ... 0.791821 0.000000 0.850516\n",
"3 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000\n",
"4 0.000000 0.225489 0.000000 ... 0.000000 0.000000 0.000000\n",
"5 0.192353 0.157845 0.791821 ... 1.000000 0.115488 0.930989\n",
"6 0.817246 0.670631 0.000000 ... 0.115488 1.000000 0.000000\n",
"7 0.000000 0.000000 0.850516 ... 0.930989 0.000000 1.000000\n",
"\n",
"[8 rows x 8 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Uq737P7GRP6l"
},
"source": [
"Cosine similarity basically gives us a metric representing the cosine of the angle between the feature vector representations of two text documents. Lower the angle between the documents, the closer and more similar they are as depicted in the following figure.\n",
"\n",
"\n",
"\n",
"Looking closely at the similarity matrix clearly tells us that documents (0, 1 and 6), (2, 5 and 7) are very similar to one another and documents 3 and 4 are slightly similar to each other but the magnitude is not very strong, however still stronger than the other documents. This must indicate these similar documents have some similar features. This is a perfect example of grouping or clustering that can be solved by unsupervised learning especially when you are dealing with huge corpora of millions of text documents."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "vh3zF8lKRP6n"
},
"source": [
"# Bonus: Clustering using Document Similarity Features\n",
"\n",
"We will use a very popular partition based clustering method, K-means clustering to cluster or group these documents based on their similarity based feature representations. In K-means clustering, we have an input parameter k, which specifies the number of clusters it will output using the document features. This clustering method is a centroid based clustering method, where it tries to cluster these documents into clusters of equal variance. It tries to create these clusters by minimizing the within-cluster sum of squares measure, also known as inertia. "
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "l5ff9gMZRP6o",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284
},
"outputId": "865209d2-1755-4cda-82f3-669c7e4e224d"
},
"source": [
"from sklearn.cluster import KMeans\n",
"\n",
"km = KMeans(n_clusters=3, random_state=0)\n",
"km.fit_transform(similarity_matrix)\n",
"cluster_labels = km.labels_\n",
"cluster_labels = pd.DataFrame(cluster_labels, columns=['ClusterLabel'])\n",
"pd.concat([corpus_df, cluster_labels], axis=1)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Document</th>\n",
" <th>Category</th>\n",
" <th>ClusterLabel</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>The sky is blue and beautiful.</td>\n",
" <td>weather</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Love this blue and beautiful sky!</td>\n",
" <td>weather</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>The quick brown fox jumps over the lazy dog.</td>\n",
" <td>animals</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A king's breakfast has sausages, ham, bacon, eggs, toast and beans</td>\n",
" <td>food</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>I love green eggs, ham, sausages and bacon!</td>\n",
" <td>food</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>The brown fox is quick and the blue dog is lazy!</td>\n",
" <td>animals</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>The sky is very blue and the sky is very beautiful today</td>\n",
" <td>weather</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>The dog is lazy but the brown fox is quick!</td>\n",
" <td>animals</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Document ... ClusterLabel\n",
"0 The sky is blue and beautiful. ... 2\n",
"1 Love this blue and beautiful sky! ... 2\n",
"2 The quick brown fox jumps over the lazy dog. ... 1\n",
"3 A king's breakfast has sausages, ham, bacon, eggs, toast and beans ... 0\n",
"4 I love green eggs, ham, sausages and bacon! ... 0\n",
"5 The brown fox is quick and the blue dog is lazy! ... 1\n",
"6 The sky is very blue and the sky is very beautiful today ... 2\n",
"7 The dog is lazy but the brown fox is quick! ... 1\n",
"\n",
"[8 rows x 3 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "S-NsBGloRP6q"
},
"source": [
"We can see from the above output that our documents were correctly assigned to the right clusters!"
]
}
]
}
================================================
FILE: notebooks/03_Text_Representation_Embedding_Models.ipynb
================================================
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "03 -Text Representation - Embedding Models.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"name": "python",
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"version": "3.7.6"
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "JS9QXHRoRiqc"
},
"source": [
"# Text Representation with Feature Engineering\n",
"\n",
"### Exploring Word Embeddings with New Deep Learning Models\n",
"\n",
"We have discussed in the previous sub-unit that Feature Engineering is the secret sauce to creating superior and better performing machine learning models. \n",
"\n",
"Traditional (count-based) feature engineering strategies for textual data involve models belonging to a family of models popularly known as the Bag of Words model. This includes term frequencies, TF-IDF (term frequency-inverse document frequency), N-grams and so on. While they are effective methods for extracting features from text, due to the inherent nature of the model being just a bag of unstructured words, we lose additional information like the semantics, structure, sequence and context around nearby words in each text document. \n",
"\n",
"This forms as enough motivation for us to explore more sophisticated models which can capture this information and give us features which are vector representation of words, popularly known as embeddings.\n",
"\n",
"Here we will explore the following feature engineering techniques:\n",
"\n",
"- Word2Vec\n",
"- GloVe\n",
"- FastText\n",
"\n",
"Predictive methods like Neural Network based language models try to predict words from its neighboring words looking at word sequences in the corpus and in the process it learns distributed representations giving us dense word embeddings. We will be focusing on these predictive methods in this article."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "wYUGSn3dRiqd"
},
"source": [
"# Prepare a Sample Corpus\n",
"\n",
"Let’s now take a sample corpus of documents on which we will run most of our analyses in this article. A corpus is typically a collection of text documents usually belonging to one or more subjects or domains."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "KH4ZwBgtRiqe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284
},
"outputId": "c285dcff-5578-4fa8-9ecf-63dba9b4f7ca"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"pd.options.display.max_colwidth = 200\n",
"\n",
"corpus = ['The sky is blue and beautiful.',\n",
" 'Love this blue and beautiful sky!',\n",
" 'The quick brown fox jumps over the lazy dog.',\n",
" \"A king's breakfast has sausages, ham, bacon, eggs, toast and beans\",\n",
" 'I love green eggs, ham, sausages and bacon!',\n",
" 'The brown fox is quick and the blue dog is lazy!',\n",
" 'The sky is very blue and the sky is very beautiful today',\n",
" 'The dog is lazy but the brown fox is quick!' \n",
"]\n",
"labels = ['weather', 'weather', 'animals', 'food', 'food', 'animals', 'weather', 'animals']\n",
"\n",
"corpus = np.array(corpus)\n",
"corpus_df = pd.DataFrame({'Document': corpus, \n",
" 'Category': labels})\n",
"corpus_df = corpus_df[['Document', 'Category']]\n",
"corpus_df"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Document</th>\n",
" <th>Category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>The sky is blue and beautiful.</td>\n",
" <td>weather</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Love this blue and beautiful sky!</td>\n",
" <td>weather</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>The quick brown fox jumps over the lazy dog.</td>\n",
" <td>animals</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A king's breakfast has sausages, ham, bacon, eggs, toast and beans</td>\n",
" <td>food</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>I love green eggs, ham, sausages and bacon!</td>\n",
" <td>food</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>The brown fox is quick and the blue dog is lazy!</td>\n",
" <td>animals</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>The sky is very blue and the sky is very beautiful today</td>\n",
" <td>weather</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>The dog is lazy but the brown fox is quick!</td>\n",
" <td>animals</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Document Category\n",
"0 The sky is blue and beautiful. weather\n",
"1 Love this blue and beautiful sky! weather\n",
"2 The quick brown fox jumps over the lazy dog. animals\n",
"3 A king's breakfast has sausages, ham, bacon, eggs, toast and beans food\n",
"4 I love green eggs, ham, sausages and bacon! food\n",
"5 The brown fox is quick and the blue dog is lazy! animals\n",
"6 The sky is very blue and the sky is very beautiful today weather\n",
"7 The dog is lazy but the brown fox is quick! animals"
]
},
"metadata": {
"tags": []
},
"execution_count": 1
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "FLojJzK0Riqi"
},
"source": [
"Let's go ahead and pre-process our text data now"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7KR8w3qbRiqi"
},
"source": [
"# Simple Text Pre-processing\n",
"\n",
"Since the focus of this unit is on feature engineering, we will build a simple text pre-processor which focuses on removing special characters, extra whitespaces, digits, stopwords and lower casing the text corpus."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "5YHHrL7VRiqj",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 185
},
"outputId": "114e4855-473e-45da-a614-88cc52a190a3"
},
"source": [
"import nltk\n",
"import re\n",
"\n",
"nltk.download('stopwords')\n",
"nltk.download('punkt')\n",
"\n",
"stop_words = nltk.corpus.stopwords.words('english')\n",
"\n",
"def normalize_document(doc):\n",
" # lower case and remove special characters\\whitespaces\n",
" doc = re.sub(r'[^a-zA-Z\\s]', '', doc, re.I|re.A)\n",
" doc = doc.lower()\n",
" doc = doc.strip()\n",
" # tokenize document\n",
" tokens = nltk.word_tokenize(doc)\n",
" # filter stopwords out of document\n",
" filtered_tokens = [token for token in tokens if token not in stop_words]\n",
" # re-create document from filtered tokens\n",
" doc = ' '.join(filtered_tokens)\n",
" return doc\n",
"\n",
"normalize_corpus = np.vectorize(normalize_document)\n",
"\n",
"norm_corpus = normalize_corpus(corpus)\n",
"norm_corpus"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n",
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['sky blue beautiful', 'love blue beautiful sky',\n",
" 'quick brown fox jumps lazy dog',\n",
" 'kings breakfast sausages ham bacon eggs toast beans',\n",
" 'love green eggs ham sausages bacon',\n",
" 'brown fox quick blue dog lazy', 'sky blue sky beautiful today',\n",
" 'dog lazy brown fox quick'], dtype='<U51')"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "2JCyCJ6GRiql"
},
"source": [
"# The Word2Vec Model\n",
"\n",
"This model was created by Google in 2013 and is a predictive deep learning based model to compute and generate high quality, distributed and continuous dense vector representations of words, which capture contextual and semantic similarity. Essentially these are unsupervised models which can take in massive textual corpora, create a vocabulary of possible words and generate dense word embeddings for each word in the vector space representing that vocabulary. \n",
"\n",
"Usually you can specify the size of the word embedding vectors and the total number of vectors are essentially the size of the vocabulary. This makes the dimensionality of this dense vector space much lower than the high-dimensional sparse vector space built using traditional Bag of Words models.\n",
"\n",
"There are two different model architectures which can be leveraged by Word2Vec to create these word embedding representations. These include,\n",
"\n",
"- The Continuous Bag of Words (CBOW) Model\n",
"- The Skip-gram Model\n",
"\n",
"## The Continuous Bag of Words (CBOW) Model\n",
"\n",
"The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words). \n",
"\n",
"Considering a simple sentence, ___“the quick brown fox jumps over the lazy dog”___, this can be pairs of __(context_window, target_word)__ where if we consider a context window of size 2, we have examples like __([quick, fox], brown)__, __([the, brown], quick)__, __([the, dog], lazy)__ and so on. \n",
"\n",
"Thus the model tries to predict the __`target_word`__ based on the __`context_window`__ words.\n",
"\n",
"\n",
"\n",
"\n",
"## The Skip-gram Model\n",
"\n",
"The Skip-gram model architecture usually tries to achieve the reverse of what the CBOW model does. It tries to predict the source context words (surrounding words) given a target word (the center word). \n",
"\n",
"Considering our simple sentence from earlier, ___“the quick brown fox jumps over the lazy dog”___. If we used the CBOW model, we get pairs of __(context_window, target_word)__ where if we consider a context window of size 2, we have examples like __([quick, fox], brown)__, __([the, brown], quick)__, __([the, dog], lazy)__ and so on. \n",
"\n",
"Now considering that the skip-gram model’s aim is to predict the context from the target word, the model typically inverts the contexts and targets, and tries to predict each context word from its target word. Hence the task becomes to predict the context __[quick, fox]__ given target word __‘brown’__ or __[the, brown]__ given target word __‘quick’__ and so on. \n",
"\n",
"Thus the model tries to predict the context_window words based on the target_word.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BHD8pUx7Riqm"
},
"source": [
"# Robust Word2Vec Model with Gensim\n",
"\n",
"The __`gensim`__ framework, created by Radim Řehůřek consists of a robust, efficient and scalable implementation of the Word2Vec model. We will leverage the same on our sample toy corpus. In our workflow, we will tokenize our normalized corpus and then focus on the following four parameters in the Word2Vec model to build it.\n",
"\n",
"- __`size`:__ The word embedding dimensionality\n",
"- __`window`:__ The context window size\n",
"- __`min_count`:__ The minimum word count\n",
"- __`sample`:__ The downsample setting for frequent words\n",
"- __`sg`:__ Training model, 1 for skip-gram otherwise CBOW\n",
"\n",
"We will build a simple Word2Vec model on the corpus and visualize the embeddings."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "-tKqisDuRiqm",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "f7106f35-82d8-4e0d-8940-02d524b4879a"
},
"source": [
"import nltk\n",
"from gensim.models import word2vec\n",
"\n",
"tokenized_corpus = [nltk.word_tokenize(doc) for doc in norm_corpus]\n",
"\n",
"# Set values for various parameters\n",
"feature_size = 15 # Word vector dimensionality \n",
"window_context = 20 # Context window size \n",
"min_word_count = 1 # Minimum word count \n",
"sample = 1e-3 # Downsample setting for frequent words\n",
"sg = 1 # skip-gram model\n",
"\n",
"w2v_model = word2vec.Word2Vec(tokenized_corpus, size=feature_size, \n",
" window=window_context, min_count = min_word_count,\n",
" sg=sg, sample=sample, iter=5000)\n",
"w2v_model"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<gensim.models.word2vec.Word2Vec at 0x7fa2e347da58>"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "6NY4l-zaRiqo",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 378
},
"outputId": "bd4ef9b9-421e-494a-9a0f-8e3985fba0a4"
},
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"\n",
"# visualize embeddings\n",
"from sklearn.manifold import TSNE\n",
"\n",
"words = w2v_model.wv.index2word\n",
"wvs = w2v_model.wv[words]\n",
"\n",
"tsne = TSNE(n_components=2, random_state=42, n_iter=5000, perplexity=5)\n",
"np.set_printoptions(suppress=True)\n",
"T = tsne.fit_transform(wvs)\n",
"labels = words\n",
"\n",
"plt.figure(figsize=(12, 6))\n",
"plt.scatter(T[:, 0], T[:, 1], c='orange', edgecolors='r')\n",
"for label, x, y in zip(labels, T[:, 0], T[:, 1]):\n",
" plt.annotate(label, xy=(x+1, y+1), xytext=(0, 0), textcoords='offset points')"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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Zu3zSSScd8nStW7eOjz536tSJrKwsFixYsE+ILleuXPx1Sbg5VZIkqTAZoku6/fpZ02Ud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\noUPj56lWrRo5OTlMmzYtfmh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"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "V7h2GmytRiqq",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "1a4fac8b-d815-4444-da85-cfbde23d1482"
},
"source": [
"w2v_model.wv['sky'], w2v_model.wv['sky'].shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([-0.8185685 , 0.3990876 , 0.02527559, -0.46214026, 0.20842107,\n",
" 0.75595075, -0.6801817 , -0.65128547, 0.23382275, 0.45040986,\n",
" 0.97311556, 1.0209339 , 1.1465698 , 0.6203446 , 1.0008206 ],\n",
" dtype=float32), (15,))"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "3INpkJK4Riqs",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 669
},
"outputId": "bebb8557-724d-40c7-bf92-bbeb5e0bf737"
},
"source": [
"vec_df = pd.DataFrame(wvs, index=words)\n",
"vec_df"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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" <td>-0.818569</td>\n",
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" <td>0.450410</td>\n",
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" <td>1.020934</td>\n",
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" <td>1.000821</td>\n",
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" <th>blue</th>\n",
" <td>-0.347705</td>\n",
"
gitextract_ngxsk4bi/
├── .gitignore
├── LICENSE
├── README.md
└── notebooks/
├── 01_Text_Wrangling_Examples.ipynb
├── 02_Text_Representation_Statistical_Models.ipynb
├── 03_Text_Representation_Embedding_Models.ipynb
├── 04_NLP_Applications_Text_Similarity_Content_Recommenders.ipynb
└── 05_NLP_Applications_Predicting_E_Commerce_Product_Recommendation_Ratings_from_Reviews_.ipynb
Condensed preview — 8 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (664K chars).
[
{
"path": ".gitignore",
"chars": 1799,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "LICENSE",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "README.md",
"chars": 122,
"preview": "# NLP Essentials\n\nEssential and Fundametal aspects of Natural Language Processing with hands-on examples and case-studie"
},
{
"path": "notebooks/01_Text_Wrangling_Examples.ipynb",
"chars": 49680,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"accelerator\": \"GPU\",\n \"anaconda-cloud\": {},\n \"colab"
},
{
"path": "notebooks/02_Text_Representation_Statistical_Models.ipynb",
"chars": 67184,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"02-Text Representation - Statist"
},
{
"path": "notebooks/03_Text_Representation_Embedding_Models.ipynb",
"chars": 289190,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"03 -Text Representation - Embedd"
},
{
"path": "notebooks/04_NLP_Applications_Text_Similarity_Content_Recommenders.ipynb",
"chars": 65089,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"accelerator\": \"GPU\",\n \"colab\": {\n \"name\": \"04-NLP"
},
{
"path": "notebooks/05_NLP_Applications_Predicting_E_Commerce_Product_Recommendation_Ratings_from_Reviews_.ipynb",
"chars": 105263,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"l"
}
]
About this extraction
This page contains the full source code of the dipanjanS/nlp_essentials GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 8 files (599.1 KB), approximately 213.1k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.