Top 6 Java NLP Libraries & Tools

• • Can we do NLP with Java?

• Does Java have a machine learning library?
• What is the natural language processing library for Java?

The answer to these questions is – Yes, Yes, and there are many. There are many top Java NLP libraries & Tools that integrate AI/ML capabilities for enabling NLP with Java. Different libraries have different use cases, which makes it essential to know their strengths and weaknesses to match the proper Java NLP library with your app needs.

Natural Language Processing in Java helps build the core logic of Java applications to read and comprehend natural language as we humans do. By integrating NLP mechanisms with AI/ML, NLPs can take real-world input, process it with NLP algorithms, and convert it to information that Java applications can interpret, manipulate and comprehend. This synergy of NLP and machine learning is essential for developing intelligent applications, making it crucial to choose the Popular Java machine learning libraries.

NLP Tools in Java are mainly used for machine translation, language translation, text summation, speech recognition, information classification, and many other use cases.

Top Java NLP Libraries & Tools – Ranked as Per GitHub Stats in 2024

Today, we have listed some of the best NLP libraries in Java with different use cases and expertise, including some that seamlessly integrate with Top Java GUI frameworks. They have been ranked as per their GitHub Ratings. However, this doesn’t indicate their efficiency over the others, as they have different use cases and features, which will be discussed in detail below. Let’s get right into the top Java NLP libraries & Tools list of 2023 –

• Deeplearning4j (13.1K stars, 5K forks)
• Stanford CoreNLP(8.5K stars, 2.8K forks)
• Apache Lucene (1.9K stars, 788 forks)
• Mallet(1.5K stars, 705 forks)
• OpenNLP(1.2K stars, 452 forks)
• LingPipe(395 stars, 200 forks)

1. DeepLearning4j

DeepLearning4j is an open-source Java library for integrating deep learning NLP capabilities in Java applications. It is an extensive toolkit with many implementations for models like CNN and RNN, transformers and much more. This NLP for Java is primarily built to be used in business environments instead of research tools and focuses on getting enterprise-grade NLP tools to the JVM ecosystem.

Key Features of DeepLearning4j

• Supports neural networks & computational nets
• GPU Acceleration via OpenCL and CUDA
• Integration with Apache Spark and Hadoop
• Import Support from Keras & TensorFlow
• APIs for Clojure and Scala

Use Cases of DeepLearning4j

• Text Classification
• Image Classification
• Time Series Forecasting & Anomaly Detection
• Natural Language Processing
• Distributed Deep Learning
• Sentiment Analysis
• Object Detection

2. Stanford CoreNLP

Stanford NLP is one of Java’s most popular, widely used, and one-stop natural language processing libraries. It is a comprehensive NLP toolkit developed by the Stanford NLP Group and is written in Java.

The primary goal of CoreNLP is to provide an extensible pipeline that empowers Java developers to build high-performing NLP applications, annotate unstructured texts, and leverage the full potential of this versatile library. If you’re looking to harness the capabilities of Stanford NLP or other top NLP library and tool, it’s a smart move to hire Java developers with expertise in utilizing this powerful tool for your NLP projects.

Key Features of Core NLP in Java

• Part of Speech Tagging
• Name Entity Recognition
• NLP Tokenization
• Coreference Resolution
• Relation Extraction
• Sentiment Analysis
• OpenIE

Use Cases of Core NLP in Java

• Dialog Systems and Chatbots
• Sentiment Analysis of social media, reviews & more
• Document Summarization
• Question Answering Systems
• Text Analysis and Information Extraction

3. Apache Lucene

The Apache Lucene Project develops open-source NLP for search-intensive software. They have two significant releases, Lucene Core (Java) and PyLucene (Python). Lucene Core is a Java NLP library with powerful search features, hit highlighting, spell check and advanced tokenization capabilities. Backed by Apache Software Foundation, it has an active and relevant community working on each release.

Key Features of Apache Lucene in Java

• Full-Text Search
• Document Ranking
• API Integration
• Text Analysis
• Flexible Querying

Use Cases of Apache Lucene

• Search in web applications
• Building custom search in mobile apps
• Search for Intranet Enterprises
• Vertical search engines
• Analytics search use cases
• NoSQL database search

4. MALLET

MALLET, or Machine Learning for LanguagE Toolkit, is another top Java NLP toolkit focusing on statistical natural language processing, topic modelling, information extraction and other such use cases.

It was developed at the University of Massachusetts Amherst with noteworthy contributions from some graduates at the University of Pennsylvania.

Key Features of MALLET

• Text classification with NLP algorithms like Maximum Entropy and Bayes
• Sequence Tagging for Name Entry Recognition
• Topic Modelling with algorithms like LSA, Pachinko Allocation & LDA
• Document Clustering
• Function and Sequence Data Optimization

Use Cases of MALLET

• Sentiment Analysis
• Document Classification
• Topic Modelling
• Analysis of Text Corpora
• Named Activity Recognition
• Sequence Modelling for POS Tagging
• Extracting User Interests Based on Activities

5. OpenNLP

Apache OpenNLP is a machine learning-based NLP toolkit that works on processing natural language text. It is an all-purpose NLP library by Apache that handles tasks like sentence segmentation, tokenization, POS tagging, chunking, parsing and much more.

A group of volunteers is developing the Apache OpenNLP project and are actively looking for new contributors to work on different aspects of the project. The group has specified their code conventions and provided the most fitting formatter and style files to ensure code consistency, which helps with global teams.

Key Features of OpenNLP

• NLP Tokenization
• POS Tagging
• Named Entity Extraction
• Data Chunking
• Natural Language Parser
• Sentence Segmentation
• Language Detection

Use Cases of OpenNLP

• Text Analysis
• Information Extraction and Retrieval
• Document Classification and Clustering
• Question Answering Systems
• Building Chatbot Agents

Also Read: – Design Patterns in Java

6. LingPipe

LingPipe is a standard NLP toolkit for Java primarily used for text processing using computational linguistics. It is ideal for apps that need features such as searching particular data points such as the name of people, locations, organizations or other information from online content.

It doesn’t limit its capabilities to finding such information, and it can also classify and analyze sentiments of popular social media sites such as Twitter. You can learn extensively about LingPipe by purchasing/renting one of the best natural language processing books, “Natural Language Processing with Java and LingPipe Cookbook”, co-authored by Breck Baldwin and Krishna Dayanidhi.

Key Features of LingPipe

• Text Classification
• Topic Modelling
• Language Identification
• Sentence Detection
• Natural Language Detection
• NLP Tokenization
• Spelling Correction
• Natural Language Parser
• Clustering Algorithms
• Name Entity Recognition
• Query Classification
• Term Extraction
• Message Filtering

Use Cases of LingPipe

• Sentiment Analysis
• Spam/Offensive Content Detection
• Authorship Identification
• Text Summarization
• Word Sense Disambiguation
• Keyword Extraction for Content Tagging

Wrapping Up!

These are the top Java NLP packages as per GitHub Ratings in 2024. Ensure you choose the right NLP tool for enabling natural language processing in your Java application. You can use different tools for different requirements, but consult with experienced Java consultants to get the best results.