AI, NLG, and Machine Learning
Top 10 NLP Libraries
Natural language processing (NLP) is the artificial intelligence (AI) method of communicating with a system using natural language. Python is one of the widely used NLP languages, and it is implemented in almost all fields and domains. In this article, we list 10 important Python NLP language libraries.
By Akshat Gupta
July 8, 2020
Natural language processing (NLP) is the artificial intelligence (AI) method of communicating with a system using natural language. In simple words, it is the blend of computer science, artificial intelligence, and human language. Applications of NLP can be witnessed in chatbots, voice assistants like Siri, Cortana, Google Translate, and the like. Python is one of the widely used languages, and it is implemented in almost all fields and domains. In this article, we list 10 important Python NLP language libraries.
1. Natural Language Toolkit (NLTK)
NLTK is one of the leading platforms for building Python programs that can work with human language data. It presents a practical introduction to programming for language processing. NLTK comes with a host of text- processing libraries for sentence detection, tokenization, lemmatization, stemming, parsing, chunking, and POS tagging.
NLTK provides easy-to-use interfaces to over 50 corpora and lexical resources. The tool has the essential functionalities required for almost all kinds of natural language processing tasks with Python.
Gensim is a Python library designed specifically for “topic modeling, document indexing, and similarity retrieval with large corpora.” All algorithms in gensim are memory-independent, with regard to the corpus size and, hence, it can process input larger than RAM. With intuitive interfaces, gensim allows for efficient multicore implementations of popular algorithms, including online Latent Semantic Analysis (LSA/LSI/SVD), latent Dirichlet allocation (LDA), random projections (RPs), hierarchical Dirichlet process (HDP), or word2vec deep learning.
Gensim features extensive documentation and Jupyter Notebook tutorials. It largely depends on NumPy and SciPy for scientific computing. Thus, you must install these two Python packages before installing gensim.
Stanford CoreNLP comprises an assortment of human language technology tools. It aims to make the application of linguistic analysis tools to a piece of text easy and efficient. With CoreNLP, you can extract all kinds of text properties (like named-entity recognition, part-of-speech tagging, etc.) in only a few lines of code.
Since CoreNLP is written in Java, it demands that Java be installed on your device. However, it does offer programming interfaces for many popular programming languages, including Python. The tool incorporates numerous Stanford’s NLP tools, like the parser, sentiment analysis, bootstrapped pattern learning, part-of-speech (POS) tagger, named entity recognizer (NER), and coreference resolution system, to name a few. Furthermore, CoreNLP supports five languages apart from English—Arabic, Chinese, German, French, and Spanish.
spaCy is an open-source NLP library in Python. It is designed explicitly for production usage—it lets you develop applications that process and understand huge volumes of text.
spaCy can preprocess text for deep learning. It can be used to build natural language understanding systems or information extraction systems. spaCy is equipped with pre-trained statistical models and word vectors. It can support tokenization for over 49 languages. spaCy boasts state-of-the-art speed, parsing, named entity recognition, convolutional neural network models for tagging, and deep learning integration.
TextBlob is a Python (2 & 3) library designed for processing textual data. It focuses on providing access to common text-processing operations through familiar interfaces. TextBlob objects can be treated as Python strings that are trained in natural language processing.
TextBlob offers a neat API for performing common NLP tasks, like part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, language translation, word inflection, parsing, n-grams, and WordNet integration.
Pattern is a text processing, web mining, natural language processing, machine learning, and network analysis tool for Python. It comes with a host of tools for data mining (Google, Twitter, Wikipedia API, a web crawler, and an HTML DOM parser), NLP (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), and network analysis by graph centrality and visualization.
Pattern can be a powerful tool both for a scientific and a non-scientific audience. It has a simple and straightforward syntax—the function names and parameters are chosen in a way so that the commands are self-explanatory. While Pattern is a highly valuable learning environment for students, it serves as a rapid development framework for web developers.
Pronounced as pineapple, PyNLPl is a Python library for natural language processing. It contains a collection of custom-made Python modules for NLP tasks. One of the most notable features of PyNLPl is that it features an extensive library for working with FoLiA XML (Format for Linguistic Annotation).
PyNLPl is divided into different modules and packages, each useful for both standard and advanced NLP tasks. While you can use PyNLPl for basic NLP tasks, like extraction of n-grams and frequency lists, and to build a simple language model, it also has more complex data types and algorithms for advanced NLP tasks.
Vocabulary is a Python library for natural language processing, which is basically a dictionary in the form of a Python module. Using this library, for a given word, you can get its meaning, synonyms, antonyms, part of speech, translations, and other such information. This library is easy to install and is a decent substitute for WordNet.
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.
MontyLingua is designed to make things as easy as possible. You feed in raw text, and you’ll receive some semantic interpretation of that text—out of the box. Instead of relying on complex machine learning techniques, it comes equipped with so- called common sense knowledge, i.e., rules and knowledge about everyday contexts that enrich the interpretations of the system. Well, to be fair, these rules are based on the Open Mind Common Sense project, which heavily relies on AI.
The tools, written in Python and also available in Java, consist of six modules that provide tokenization, tagging, chunking, phrase extraction, lemmatization, and language generation capabilities.