JaTeCS an open-source JAva TExt Categorization System

TL;DR

JaTeCS is an open-source Java library that streamlines research in automatic text categorization by providing comprehensive tools for data handling, processing, machine learning, and experimental setup, facilitating rapid experimentation.

Contribution

It introduces a modular, extensible Java library that covers all steps of text categorization experiments, including data reading, NLP tools, machine learning, and evaluation, with ready-to-use templates.

Findings

Supports multiple text formats and languages

Includes a wide range of machine learning algorithms

Provides ready-to-use experimental templates

Abstract

JaTeCS is an open source Java library that supports research on automatic text categorization and other related problems, such as ordinal regression and quantification, which are of special interest in opinion mining applications. It covers all the steps of an experimental activity, from reading the corpus to the evaluation of the experimental results. As JaTeCS is focused on text as the main input data, it provides the user with many text-dedicated tools, e.g.: data readers for many formats, including the most commonly used text corpora and lexical resources, natural language processing tools, multi-language support, methods for feature selection and weighting, the implementation of many machine learning algorithms as well as wrappers for well-known external software (e.g., SVM_light) which enable their full control from code. JaTeCS support its expansion by abstracting through interfaces many of the typical tools and procedures used in text processing tasks. The library also provides a number of "template" implementations of typical experimental setups (e.g., train-test, k-fold validation, grid-search optimization, randomized runs) which enable fast realization of experiments just by connecting the templates with data readers, learning algorithms and evaluation measures.