Welcome back to Part 2 of our Natural Language Processing series. As we told you in the beginning these sessions are going to be a mix of both theoretical and practical, so the first thing we need to do is to set our machines for NLP and learn about various libraries that Python has to offer for NLP.
If you are new to NLP, then go ahead to Part 1 Introduction to NLP - Getting Started and learn about the basics of Natural Language Processing, key terminologies and why we need NLP.
Prerequisites
1. Python - 3.7 and above
Libraries for NLP
Python being an open-source programming language offers a wide range of libraries that can be used for Natural Language Processing(NLP). Here is the list of libraries present in Python for NLP.
1. Natural Language Toolkit (NLTK):-
The most common library in Python for NLP is NLTK (Natural Language Toolkit), as it supports a wide range of languages. Not only this, being an open source it is freely available to students, teachers, aspirants and to all thus providing a huge active community support.
It offers user-friendly interfaces to more than 50 corpora and lexical resources, including WordNet. Additionally, it provides a set of text-processing libraries that cover tasks such as classification, tokenization, stemming, tagging, parsing, and semantic reasoning. Furthermore, it includes wrappers for robust NLP libraries used in industrial applications.
Installation
pip install -U nltk
2. spaCy:-
Another popular library written in Python is spaCy, an open-source library for advanced Natural Language Processing. It comes loaded with features and is faster than NLTK for large datasets. It provides support for over 73 languages, has around 84 trained pipelines for different languages, and taps into the power of transformers like BERT for multitasking. It can work efficiently with named entity recognition, syntax visualization etc. Apart from this it provides support for frameworks like PyTorch, Tenserflow and many other frameworks. It also provides support for deployment and a production-ready training system.
Installation
pip install -U spacy
3. TextBlob:-
It is one of the most easy to use Python libraries for Natural Language Processing. As per the official documents, Textblob is defined as:-
TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.
TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.
Installation
pip install -U textblob
4. Gensim:-
Gensim stands out as a specialized Python library crafted for tasks such as "topic modeling, document indexing, and similarity retrieval with large corpora." Notably, Gensim's algorithms are designed to be memory-independent, making it capable of processing input sizes beyond the constraints of RAM. Its intuitive interfaces facilitate efficient multicore implementations of popular algorithms, encompassing online Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP), or word2vec deep learning.
This library provides extensive documentation and Jupyter Notebook tutorials, offering valuable resources for users. Gensim relies significantly on NumPy and SciPy for scientific computing, necessitating the installation of these Python packages before incorporating Gensim into your workflow.
Installation
pip install -U gensim
Last but certainly not least, we delve into the realm of the most widely used Python library in Machine Learning: scikit-learn. This versatile library serves as the backbone of Spotify's app, leveraging its machine-learning algorithms and spam detection functions to curate a highly refined user experience.
Yet, sci-kit-learn's influence extends far beyond the world of music streaming. Renowned for its exceptional adaptability, this library excels in diverse domains, offering robust capabilities such as text classification, supervised machine learning, and sentiment analysis.
Installation
pip install -U scikit-learn
Summary
In the further sections, we will go through all these libraries individually and make you familiar with them. If are still not familiar with the basics of Natural Language Processing (NLP), I strongly recommend going through Part 1 Introduction to NLP before diving further into the topic.
Next, we will be starting with the NLTK library and learning it from start to end with practical's before moving further with the other libraries.
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