7 Top NLP Libraries Java Developers Should Know In 2019

NLP libraries Java

Java is one of the most widely used programming languages and with emerging technologies, natural language processing plays a crucial role in several domains including healthcare, e-commerce, etc. In this article, we list down 7 top-rated NLP libraries for Java developers.

(The list is in alphabetical order)

1| Apache OpenNLP

The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It contains several components such as sentence detector, tokenizer, name finder, document categorizer, part-of-speech tagger, chunker, parser, etc., enabling one to build a full NLP pipeline. The library supports the most common NLP tasks such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection, and coreference resolution.


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2| Apache UIMA

Unstructured Information Management Applications (UIMA) is a component architecture and software framework implementation for the analysis of unstructured content like text, audio, and video data. The goal of UIMA is to transform unstructured information to structured information by orchestrating analysis engines to detect entities or relations and thus to build the bridge between the unstructured and the structured world. UIMA additionally provides capabilities to wrap components as network services and can scale to very large volumes by replicating processing pipelines over a cluster of networked nodes.

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3| GATE Embedded

General Architecture for Text Engineering (GATE) is an open source software toolkit which is capable of solving almost any text processing system. GATE Embedded is an object-oriented open source framework (or class library) implemented in Java which is used in all GATE-based systems and forms the core elements of GATE developer. It is designed to allow you to embed language processing functionality in diverse applications.

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4| LingPipe

LingPipe is a toolkit for processing text using computational linguistics. The tasks such as finding the names of people, organizations or locations in news, automatically classify Twitter search results into categories, etc. can be easily done by this toolkit. The architecture of LingPipe is designed to be efficient, scalable, reusable and robust. It has Java API with source code and unit tests, n-best output with statistical confidence estimates, thread-safe models and decoders for concurrent-read exclusive-write (CREW) synchronization, etc.  

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MALLET is an open source Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. It includes tools for document classification, efficient routines for converting text to features, a wide variety of machine learning algorithms, code for evaluating classifier performance, tools for sequence tagging for applications, routines for transforming text documents into numerical representations that can then be processed efficiently, etc.

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6| NLP4J

Natural Language Processing for JVM languages (NLP4J) project provides NLP tools readily available for research in various disciplines, frameworks for fast development of efficient and robust NLP components, API for manipulating computational structures in NLP (e.g., dependency graph). The project is initiated and currently led by the Emory NLP research group and is under the Apache 2 license.

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7| Stanford CoreNLP

Stanford CoreNLP provides a set of natural language analysis tools written in Java. It provides a set of human language technology tools which gives the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, indicates sentiments, indicates which noun phrases refer to the same entities, etc. Stanford CoreNLP is an integrated framework, which makes it very easy to apply a bunch of language analysis tools to a piece of text. It provides an integrated NLP toolkit with a broad range of grammatical analysis tools, fast, robust annotator for arbitrary texts, support for a number of major (human) languages, ability to run as a simple web service, etc.

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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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