TopicModel4J: A Java Package for Topic Models

• URL: http://arxiv.org/abs/2010.14707v1
• Date: Wed, 28 Oct 2020 02:33:41 GMT
• Title: TopicModel4J: A Java Package for Topic Models
• Authors: Yang Qian, Yuanchun Jiang, Yidong Chai, Yezheng Liu, Jiansha Sun
• Abstract summary: We design and implement a Java package, TopicModel4J, which contains 13 kinds of representative algorithms for fitting topic models. The package provides an easy-to-use interface for data analysts to run the algorithms, and allow to easily input and output data.
• Score: 2.519906683279153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Topic models provide a flexible and principled framework for exploring hidden structure in high-dimensional co-occurrence data and are commonly used natural language processing (NLP) of text. In this paper, we design and implement a Java package, TopicModel4J, which contains 13 kinds of representative algorithms for fitting topic models. The TopicModel4J in the Java programming environment provides an easy-to-use interface for data analysts to run the algorithms, and allow to easily input and output data. In addition, this package provides a few unstructured text preprocessing techniques, such as splitting textual data into words, lowercasing the words, preforming lemmatization and removing the useless characters, URLs and stop words.

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