
Natural language processing (NLP) is a field of deep learning whose goal is to teach the computer how to comprehend human languages. This field is a union of Data Science and machine learning which basically deals with the extraction and analysis of text data to extract some values from it.
NLP is an amazing technology to learn in 2021 as many big companies are focusing on the sentiment analysis of their customers or making advanced chatbots using raw text data.
So if this field excites you, in this article, I have covered 7 amazing Python libraries that might help you implement NLP algorithms and build projects with them.
1. NLTK
Natural language toolkit or NLTK is by far the most popular platform for building NLP related projects. It provides an easy to use interface to over 50 corpora and lexical resources and comes with an array of text processing libraries like classification stemming tagging parsing tokenization etc.
This library is also an open-source library and is available for almost every kind of operating system. So whether you are a beginner to NLP or an ML researcher you can definitely learn NLTK.
Installation
pip install nltk
Learn more: https://www.nltk.org/
2. polyglot
Polyglot is a Python library for NLP which is especially useful as it supports extensive multilingual applications. According to the documentation of polyglot, it supports tokenization for 165 languages, language detection for 196 languages, part-of-speech tagging for 16 languages and sentiment analysis in more than 130 languages.
So it might be useful if someone is working with non-mainstream language. Also, it works very fast as it uses NumPy.
Installation
pip install polyglot
Learn more: http://polyglot.readthedocs.org/
3. SpaCy
SpaCy is a Python NLP library that can be useful specifically for industry-level real-world projects containing huge amounts of text data.
The main advantage of using this library is its speed. SpaCy is much faster than other libraries as it is written in Cython which also makes it capable of handling larger amounts of data efficiently.
Support for more than 64 languages, 60 + train pipelines for 19 languages, multi-task learning with pre-trained transformers like BERT and support for modern ML/DL frameworks like Pytorch and Tensorflow makes SpaCy – a good choice for professional projects.
Installation(along with dependencies)
pip install –U setuptools wheel
pip install –U spacy
python -m spacy download en_core_web_sm
Learn more: https://spacy.io/
4. GenSim
GenSim is an NLP library written in Python that is popular due to its amazing speed and memory optimization. All the libraries used in GenSim are memory independent and can run easily with data sets of large size also. It comes with mini useful NLP algorithms like random projections (RP), latent semantic analysis (LSA), hierarchical Dirichlet process (HDP) etc.
GenSim uses SciPy and NumPy for computing and is used in applications like chatbots and semantic search applications etc.
Installation
pip install - upgrade gensim
Learn more: https://radimrehurek.com/gensim/
5. Textblob
Textblob is a Python library that is powered by NLTK. It provides almost all the functionalities of NLTK but in a much simpler and beginner-friendly manner and its API can be used for some common tasks like classification, translation, word inflexion etc.
Many data scientists also use textblob for prototyping as it is much more lightweight to work with.
Installation
pip install -U textblob
python -m textblob.download_corpora
Learn more: https://textblob.readthedocs.io/en/dev/
6. PyNLPI
PyNLPI also pronounced as pineapple is a Python NLP library that is mainly used for building basic language processing models. It is divided into different models and packages which can be used for different varieties of NLP tasks. One of the most prominent features of PyNLPI is that it comes with an entire library for working with FoLiA XML(format for linguistic annotation)
Installation
pip install pynlpl
Learn more: https://pynlpl.readthedocs.io/en/latest/
7. Pattern
Pattern is a multipurpose Python library that can be used for different tasks like natural language processing (tokenization sentiment analysis POS tagging etc.), Data mining from websites and machine learning using built-in models such as K-nearest Neighbors, Support Vector Machine etc.
This library is easy to understand and implement for beginners due to its simple and straightforward syntax and it is also helpful for web developers who need to work with text data.
Installation
pip install pattern
Learn more: https://github.com/clips/pattern
Conclusion
Although almost all of the NLP libraries mentioned in the list form similar tasks they might come in handy in some specific cases. Like SpaCy might be helpful while working on a real-world project with large data sets, GenSim will come into play when there are strict memory constraints.
NLTK will definitely be the most popular library for students and for research purposes. So we should always choose the library that is suits the problem statement the most.
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