Correlated Concept based Topic Updation Model for Dynamic Corpora

Abstract

A rapid growth of documents available on the Internet, digital libraries, medical documents, news wires and other scientific document corpuses has motivated the researchers to propose many text mining techniques that help users to quickly retrieve trace and summarize the information in an effective way. Topic detection is one such technique which discovers precise, meaningful and concise labels for the formulated static document clusters. This technique helps the user to navigate and retrieve the needed information quickly and efficiently. Topic updation is the process of identifying and renewing the discovered labels whenever the document clusters are updated dynamically. This paper focuses on topic updation model based on Testor theory. The proposed work is experimented using 20newsgroup and scientific literature data set. The experimental results demonstrate that the proposed algorithm exhibit better performance, compared to the existing algorithms for topic detection.

Conceptualization topic modeling

“…Unsupervised non-hierarchical topic models are widely studied, such as LDA [Blei et al, 2003b], Probase-LDA [Yao et al, 2015] , TCC [Jayabharathy et al, 2014] and COT [Yao et al, 2016] etc. The most famous one is Latent Dirichlet Allocation (LDA).…”