Java has been one of the most widely used programming languages in the industry for more than a decade. From mobile applications to deep learning, Java has offered a variety of tools and libraries for its developers. Java natural language processing is just another feather in the cap for Java. With emerging technologies, natural language processing plays a very important role in numerous domains like traveling, healthcare, and e-commerce. Java natural language processing allows Java developers to implement this technology in countless Java applications. In this article, we will be discussing the best 9 Java natural language processing libraries and tools.
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See Also: 20 Useful Libraries Java Programmers Should Know
Before starting, let’s shed some light on what NLP (Natural Language Processing) is. A formal definition of NLP usually includes terms like Artificial Intelligence (AI), and formal linguistics concepts that specify analyzing of natural languages whereas in somewhat simpler terms it can be described as a set of tools that are used to derive useful information from a natural language source, such as a web page, a file or a text document. A user query is processed using NLP techniques to generate the most accurate results as per the need of the user. Modern search engines like google have been very successful in this regard.
The semantics of a sentence is present its meaning. An English speaker would easily understand the meaning of the sentence and can perform the respective action like, “Pass the ball” However, sentences can also be ambiguous sometimes and their meaning can only be understood from the context. This is what makes the NLP, a complex task for a machine as it will be missing the context.
Natural language processing is commonly used to enhance the utility of an application Searching is one of the most common examples but it also has some good usage for applications like
Following are the top 9 Java natural language processing libraries.
Apache OpenNLP is an open-source Natural Language Processing Java library. It is a machine learning-based toolkit for processing natural language text. It consists of a set of components including a sentence detector, tokenizer, name finder, document categorizer, part-of-speech tagger, chunker, and a parser that allows Java developers to build a complete NLP pipeline.
This library can be used to perform all the common NLP tasks such as sentence segmentation, part-of-speech tagging, named entity recognition, tokenization, natural language detection, chunking, parsing, and coreference resolution.
Apache UIMA short for Unstructured Information Management Applications is a component architecture and software framework implementation written in C++ and Java. It was originally made by IBM, Apache Software Foundation, and OASIS for the analysis of unstructured content including text, audio, and videos.
UIMA was designed to transform the unstructured information into a structured one by orchestrating analysis engines for first detecting all the entities or relations and then building the bridge between them to form structured data. UIMA also offers features to wrap components as the network services and it can also be scaled to deal with very large volumes of data by replicating the processing pipelines over a cluster of networked nodes.
General Architecture for Text Engineering or GATE is an open-source software toolkit for Java natural language processing that can be used to solve almost every type of text processing system. It is an object-oriented open source framework implemented in Java. GATE is one of the matured NLP tools as it has been in development for more than a decade.
It includes resources for all the common LE data structures and algorithms, a complete set of language analysis tools for Information Extraction, and a range of data visualization and editing tools. It features all the common NLP modules like a tokenizer, a sentence splitter, a gazetteer, a part of speech tagger, a named entities transducer, and a coreference tagger. It is designed in such a way that allows Java developers to embed several language processing functionalities in their applications.
LingPipe is a basic Java toolkit for processing text using computational linguistics. It is often used in applications that involve searching tasks such as finding the names of people, organizations, or locations from online news content, automatically classifying and analyzing sentiments of the Twitter search results, etc.
The architecture of this toolkit makes it very efficient, stable, scalable, and robust. It also includes some other Java API with source code and unit testing features, thread-safe models, and decoders for concurrent-read exclusive-write (CREW) synchronization.
MALLET is another open-source Java-based package ideal for statistical natural language processing. It also offers more features like information classification, clustering, topic modeling, information extraction, and other machine learning applications, especially for text. It includes 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.
Natural Language Processing for JVM languages commonly known as NLP4J provides a set of NLP tools that are readily available for research in various disciplines of NLP. It also offers some frameworks for the fast development of efficient and robust NLP components as well API for manipulation of computational structures in NLP. The NLP4J was initiated and is also currently led by the Emory NLP research group under the Apache 2 license.
Stanford CoreNLP is an extensible annotation-based Java natural language processing pipeline that provides a set of natural language analysis tools written in Java. Stanford CoreNLP is an integrated framework, that makes it very convenient to apply various language analysis tools to a piece of text. This open-source toolkit is widely used among commercial and government users of open-source NLP technologies.
It provides an integrated NLP toolkit with a broad range of grammatical analysis tools, fast, robust annotator for arbitrary texts. It can take basic human language text and can be used to identify the base forms of words, their parts of speech, whether they are names of any well-known companies or people. It can also normalize dates, times, and numeric quantities present in the text and indicate which noun phrases refer to the same entities. The basic distribution provides the model files for the analysis of English, language but the engine is compatible with models for other languages as well.
Apache Lucene is an open-source high-performance, full-featured information retrieval library written completely in Java. It is an ideal choice of tool for almost any application that requires the full-text search option, especially for cross-platform applications.
Apache Lucene has set the standard for searching and indexing performance to the next level. It is fully supported by the Apache Software Foundation and was released under the Apache Software License. After a rapid increase in its user Java base, It has also been ported to several other programming languages that include Python, Delphi, Perl, C#, C++, Ruby, and PHP.
Last on our list is ReVerb, an NLP program that is known for automatically identifying and extracting binary relationships from English sentences. It is a system that extracts relational triples from the text. It was primarily designed for Web-scale data extraction, due to which, it offers a significantly faster processing speed than other NLP tools. A collection of more than 15 million ReVerb extractions is also available online to be used for research and academic purpose.
It comes with a command Line Interface that takes text or HTML as input and returns a tab-separated table of results where each row represents a single extracted (argument1, relation phrase, argument2) triple, along with the metadata. It also comes with the Java Interface and also includes a class called ReVerbClassifierTrainer for training new confidence functions, given a list of labeled examples. It is a great option if you are learning NLP and want to explore it further.
All the above-mentioned java natural language processing tools are unique and are one of the best options available for performing the respective tasks. If you are currently exploring Java natural language processing, it is highly recommended to try out and experiment with all these toolkits and libraries to better understand the various applications of Java natural language processing with these remarkable tools.
Shaharyar Lalani is a developer with a strong interest in business analysis, project management, and UX design. He writes and teaches extensively on themes current in the world of web and app development, especially in Java technology.
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