TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information.

ABSTRACT: With the exponential growth in the daily publication of scientific articles, automatic classification and categorization can assist in assigning articles to a predefined category. Article titles are concise descriptions of the articles' content with valuable information that can be useful in document classification and categorization. However, shortness, data sparseness, limited word occurrences, and the inadequate contextual information of scientific document titles hinder the direct application of conventional text mining and machine learning algorithms on these short texts, making their classification a challenging task. This study firstly explores the performance of our earlier study, TextNetTopics on the short text. Secondly, here we propose an advanced version called TextNetTopics

SUBMITTER: Voskergian D 

PROVIDER: S-EPMC10585361 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature