In this article, we will see python NLP libraries which are most commonly used in natural language processing or data science or machine learning projects. We will look at the functionalities of each library and also learn about the specific NLP tasks which can be accomplished with those libraries.
So let’s start this article and learn about different libraries.
Python NLP Libraries
Natural Language Toolkit (NLTK)
It is the most fundamental and leading platform used for building programs that deal with human language data. NLTK provides resources like pre-trained models and corpora along with a set of libraries for various operations like text classification, text tokenization, and text stemming. This library was developed at the University of Pennsylvania. The main feature of NLTK is its versatility, on the other hand, people can have a headache because of its slowness, causing a delay in fast-paced production.
This library has the capability of handling large data with its main tasks being topic modeling, similarity retrieval, and document indexing. Gensim uses memory independent algorithms which makes it highly efficient in terms of memory usage and processing speed. Most importantly, Gensim is robust and scalable as well.
This library has a unique feature of working with large multilingual applications. Its multilingual feature has enabled it to support over 200 languages for different operations. Polyglot uses the utility of numpy for delivering its fast service. This library can be used for operations like sentiment analysis, part of speech tagging and language detection. Polyglot has its own dedicated command line for performing optimally.
This library is used for processing and managing textual data. TextBlob is highly recommended for beginners, as it helps in easy interface with NLTK. TextBlob library also helps in performing beginner NLP tasks. Similar to NLTK, TextBlob is also slow to use and thus it is not used by developers for production tasks.
This java-based library is developed by Stanford University. CoreNLP also has wrappers for other languages like Python. Stanford CoreNLP can perform operations like part-of-speech (POS) tagging, entity recognition, pattern learning, parsing, and much more. Amongst other libraries, CoreNLP is fast and the main aim of this library is to simplify the advanced concepts of NLP.
A relatively new library with support for fewer languages and lesser options to work with. The aim of Spacy is “less is more”. Spacy is predicted to be the library for the future because of its minimalism and efficient development of projects. Spacy has its use in preprocessing text for deep learning applications. Along with this, spacy is useful for building information extraction systems.
A data mining library which helps in parsing different websites for extracting data. Pattern also has numerous tools for performing tasks related to natural language processing, machine learning, network analysis, and visualization. The main functionality as discussed is crawling the web, thus we can say that it has limited usage.
Vocabulary is a python library for natural language processing projects, working as a dictionary for various operations. Using Vocabulary, we can find meanings, synonyms, antonyms, part of speech, translations and other related details of the desired word.
As the name suggests, this library helps in converting the natural language questions into queries in a database. Using Quepy, we can map different query languages in an easy manner. With the help of Quepy, we can create question answering systems, chatbots, and information extracting systems.
iNLTK (Natural Language Toolkit for Indic Languages)
iNLTK is a library for all those beginners and developers who are looking to work with languages of the Indian subcontinent. iNLTK supports 13 languages with major languages like Hindi, Kannada, Punjabi, Sanskrit, etc. iNLTK can facilitate NLP operations like tokenization, word embeddings, text completion and similarity of sentences.
Indic NLP Library
This library is quite similar to iNLTK, the difference is in the count of languages supported by the Indic NLP library. It supports over 15 languages in two different categories of Indo-Aryan and Dravidian languages. Indic NLP library can perform operations like Text Normalization, Script Information, Translation, Transliteration, etc.
So we have looked at the list of python NLP libraries used in natural language processing projects. We also explored the different projects which can be built using each library. One advice that I would give is to use these libraries once you are well versed in your NLP concepts and fundamentals.