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Top 10 Python NLP Libraries For 2019

With the help of Natural Language Processing, an organisation can gain valuable insights, patterns, and solutions. Python is one of the widely used languages and it is implemented in almost all fields and domains. In this article, we list down 10 important Python Natural Language Processing Language libraries.

1|  Natural Language Toolkit (NLTK)

NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, etc. This library provides a practical introduction to programming for language processing. NLTK has been called “a wonderful tool for teaching and working in computational linguistics using Python,” and “an amazing library to play with natural language.”

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2| Gensim

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is basically the natural language processing (NLP) and information retrieval (IR) community. The features of this library include such as all algorithms are memory-independent w.r.t. the corpus size, intuitive interfaces, efficient multicore implementations of popular algorithms, distributed computing, etc.

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3| polyglot

Polyglot is a natural language pipeline which supports massive multilingual applications. The features include tokenisation, language detection, named entity recognition, part of speech tagging, sentiment analysis, word embeddings, etc. Polyglot depends on Numpy and libicu-dev, on Ubuntu/Debian Linux distribution you can install such packages by executing the following command:

sudo apt-get install python-numpy libicu-dev

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4| TextBlob

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, WordNet integration, parsing, word inflection, adds new models or languages through extensions, and more.

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5| CoreNLP

Stanford CoreNLP provides a set of human language technology tools. Stanford CoreNLP’s goal is to make it very easy to apply a bunch of linguistic analysis tools to a piece of text. Stanford CoreNLP integrates many of Stanford’s NLP tools, including the part-of-speech (POS) tagger, the named entity recognizer (NER), the parser, the coreference resolution system, sentiment analysis, bootstrapped pattern learning, and the open information extraction tools. The tools variously use rule-based, probabilistic machine learning, and deep learning components.

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6| spaCy

spaCy is a library for advanced Natural Language Processing in Python and Cython which comes with a number of interesting features. spaCy comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages. It features state-of-the-art speed, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration.

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7| Pattern

Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter, and Wikipedia API, a web crawler, an HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis by graph centrality and visualization. Pattern supports Python 2.7 and Python 3.6.

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8| Vocabulary

Vocabulary is a Python library for natural language processing which is basically a dictionary in the form of Python module. Using this library, for a given word you can get its meaning, synonyms, antonyms, part of speech, translations and other such. This library is easy to install and is a decent substitute to Wordnet.

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9| PyNLPl

PyNLPl, pronounced as ‘pineapple’, is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build a simple language model. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).

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10| Quepy

Quepy is a python framework to transform natural language questions into queries in a database query language. It can be easily customized to different kinds of questions in natural language and database queries. Quepy uses an abstract semantics as a language-independent representation that is then mapped to a query language. This allows your questions to be mapped to different query languages in a transparent manner.

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Ambika Choudhury
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.

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