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Title

Polynomial Topic Distribution with Topic Modeling for Generic Labeling

Abstract

Topics generated by topic models are typically reproduced as a list of words. To decrease the cognitional overhead of understanding these topics for end-users, we have proposed labeling topics with a noun phrase that summarizes its theme or idea. Using the WordNet lexical database as candidate labels, we estimate natural labeling for documents with words to select the most relevant labels for topics. Compared to WUP similarity topic labeling system, our methodology is simpler, more effective, and obtains better topic labels.

Keywords

Text mining, Topic model, Topic label, LDA, WordNet

Authors

Syeda Sumbul Hossain, Md. Rezwan Ul-HassanShadikur Rahman

Phone

Journal or Conference Name

Communications in Computer and Information Science

Publish Year

2019

Indexing

Scopus