Python Libraries and Packages for Natural Language Processing
Natural language processing (NLP) is an exciting field in data science and artificial intelligence that deals with teaching computers how to extract meaning from text data. With the help of Natural Language Processing, an organization can gain valuable insights, patterns, and solutions. In this article we’ll be touring the overview of popular python libraries and packages for Natural Language Processing.
1. Natural Language Toolkit (NLTK)
The Natural Language Toolkit library (NLTK) is one of the most popular Python libraries for natural language processing. It was developed by Steven Bird and Edward Loper of the University of Pennsylvania. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as PorterStemmer, along with a suite of text processing libraries for classification, tokenization, stemming, etc.
Its modularized structure makes it excellent for learning and exploring NLP concepts, but it’s not meant for production. It’s the most famous Python NLP library, and it’s led to incredible breakthroughs in the field.
NLTK is also popular for education and research. On its own website, NLTK claims to be an "an amazing library to play with natural language." The major drawback of NLTK is that it’s heavy and slippery, and it has a steep learning curve. The second major weakness is that it’s slow and not production-ready.
2. Scikit-learn
Scikit-learn (Sklearn) is the most powerful and famous Machine Learning library in Python. The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
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3. TextBlob
Textblob is an open-source python library for processing textual data. It provides a simple API for diving into common natural language processing tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, WordNet integration, parsing, word inflection, adds new models or languages through extensions, and more.
Textblob is built on top of NLTK and Pattern also it is very easy to use and can process the text in a few lines of code. It very usefull library for fast-prototyping or building applications that don’t require highly optimized performance.
TextBlob makes text processing simple by providing an intuitive interface to NLTK. It’s a welcome addition to an already solid lineup of Python NLP libraries because it has a gentle learning curve while boasting a surprising amount of functionality.
4. SpaCy
spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. spaCy provides a concise API to access its methods and properties governed by trained machine (and deep) learning models. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages. It’s not as widely adopted, but if you’re building a new application, you should give it a try.
SpaCy is minimal and opinionated, and it doesn’t flood you with options like NLTK does. Its philosophy is to only present one algorithm (the best one) for each purpose
5. Gensim
Gensim is most commonly used for topic modeling and similarity detection. However, it now supports a variety of other NLP tasks such as converting words to vectors (word2vec), document to vectors (doc2vec), finding text similarity, and text summarization. It is a leading and a state-of-the-art package for processing texts, working with word vector models and for building topic models.
Gensim is not a general-purpose NLP library, but for the tasks it does handle, it does them well. Its topic modeling algorithms, such as its Latent Dirichlet Allocation (LDA) implementation, are best-in-class. In addition, it’s robust, efficient, and scalable.
6. Stanford Core NLP
Stanford’s CoreNLP is a Java library with Python wrappers. It’s in many existing production systems due to its speed.
Stanford CoreNLP is a suite of production-ready natural analysis tools. It includes part-of-speech (POS) tagging, entity recognition, pattern learning, parsing, and much more. Many organizations use CoreNLP for production implementations. It’s fast, accurate, and able to support several major languages.
It comes with built-in processors to perform five basic NLP tasks:- Tokenization
- Multi-Word Token Expansion
- Lemmatisation
- Parts of Speech Tagging
- Dependency Parsing
7. Fasttext
Fasttext is a library for efficient learning of word representations and sentence classification. It combines some of the most successful concepts introduced by the natural language processing and machine learning communities in the last few decades.
FastText allows you to train supervised and unsupervised representations of words and sentences. It also employs a hierachical softmax that takes advantage of the unbalanced distribution of the classes to speed up computation. These different concepts are being used for two different tasks: efficient text classification and learning word vector representations.
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