Scopus Indexed Paper

Paper Details


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