Generation of author topic models using LDA

Abstract

Copyright and ownership of research ideas is questionable as to which author the credit should be attached to. Mining author contributions has to be approached more semantically to solve this issue. Representing the research ideas using topic distributions substantiate the measuring of author contributions. Author Topic Models (ATM) are generally obtained by applying topic modeling approaches over an author’s research articles. ATMs form the blueprints of an author. Given a research paper and the blueprints of it’s’ authors, identifying the contribution of every author in the article becomes easy. This paper proposes the generation of ATMs by applying Latent Dirichlet Allocation (LDA).