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Top 5 NLP Libraries To Use in Your Projects

Last Updated on July 4, 2022 by Editorial Team

Author(s): Rijul Singh Malik

Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.

A Blog on the most used NLP Libraries

Photo by Christina @ wocintechchat.com on Unsplash

NLP is one of the hottest fields in AI. There are numerous top-notch libraries to help you with NLP in your projects. This blog will list the top 5 libraries. It will help you with your project as well as help you learn more about NLP.

1. Introduction

Natural language processing (NLP) is a branch of artificial intelligence. It is about understanding, interpreting, processing, and generating natural language. The NLP libraries have emerged to solve the problem of designing, implementing, and deploying NLP systems. In this blog, we will discuss the top 5 NLP libraries, which you can use in your projects.

Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. As a sub-field of computer science, NLP is related to artificial intelligence, information retrieval, speech recognition, and machine translation. Given the rapid development of this field, it is an extremely wide and open area of research.

2. NLTK

Natural Language Toolkit (NLTK) is a leading platform for building Python programs to process 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, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion group and mailing list.

NLTK is the “Natural Language Tool Kit” and it’s a powerful Python library that makes working with text simple and fun. NLTK was developed by Steven Bird, Edward Loper, and Alex Rubinstein in order to meet their own research needs and is used by a wide variety of people, including students, researchers, and developers to process 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, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.

3. SpaCy

spaCy is a free and open-source library for advanced Natural Language Processing in Python and Cython. It comes with a variety of utilities for tokenization, sentence segmentation and parsing, entity recognition, and coreference resolution. The package can also be used with other languages, such as Java, using the ‘spacy_bindings’ library. It is available on PyPI and can be installed with pip. spaCy is an open-source library written in Python and Cython. It depends on the following dependencies: NumPy, Scipy, sphinx, pillow, yaml, six, pandas, requests, docopt, and jieba.

With its simple API and powerful extensions, SpaCy is easy to use for beginners and a powerful tool for experts. It can be used for tasks like part-of-speech tagging, noun phrase extraction, sentiment analysis, and much more. With its neural network-based approaches, it also performs very well with harder tasks such as semantic parsing, dependency parsing, and parsing English texts with a high degree of grammatical complexity.

4. Stanford CoreNLP

Stanford CoreNLP is a Java natural language processing toolkit that provides a set of general-purpose language analysis tools. It can be used in applications such as information extraction, question answering, information retrieval, sentiment analysis, text-message classification, and summarization.

Stanford CoreNLP is a Java-based toolkit for the Stanford Parser, a statistical parser written by the Stanford NLP Group. It can be used to find named entities, classify text into different categories, and find relations between different parts of the sentence. It consists of a command-line tool and Java library.

Stanford CoreNLP is a Java-based framework for the processing of natural language text. It can take raw text input, process it, and then spit out some structured data for you. Each of the Java classes included in the framework can be used by itself or in conjunction with the others. You can use Stanford CoreNLP to: tokenize the input text into sentences, words, and punctuation

  1. identify each word’s part-of-speech (POS)
  2. classify each token by its lexical category (e.g. noun, verb, adjective, adverb)
  3. identify named entities (e.g. people, organizations, locations, times, quantities, percentages, currency)
  4. perform syntactic analysis on the input text (parsing) * generate a structured output with the results.

5. OpenNLP

OpenNLP is a machine learning-based toolkit for the processing of natural language text. It is released under the Apache 2.0 license and is freely available for commercial and non-commercial use. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, and parsing.

OpenNLP is the de-facto standard for the best open-source Natural Language Processing tool or library. Natural Language Processing is the technology used to solve the problem of understanding human language by machines. This technology has been a big part of Artificial Intelligence research over the past decade or so. The goal of NLP is to develop a machine that can understand human language and process it in a way that is just as natural to humans.

Conclusion

In a future blog, we will cover using NLP with intent or the use case of intent in a specific industry.


Top 5 NLP Libraries To Use in Your Projects was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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