A modified model for topic detection from a corpus and a new metric evaluating the understandability of topics
Tomoya Kitano, Yuto Miyatake, Daisuke Furihata
This paper presents a modified neural model for topic detection from a corpus and proposes a new metric to evaluate the detected topics. The new model builds upon the embedded topic model incorporating some modifications such as document clustering. Numerical experiments suggest that the new model performs favourably regardless of the document's length. The new metric, which can be computed more efficiently than widely-used metrics such as topic coherence, provides variable information regarding the understandability of the detected topics.
cs.CL, cs.LG