Topic models can improve domain term extraction

Authors:
Elena Bolshakova;Natalia Loukachevitch;Michael Nokel
Affiliations:
Moscow State University, Russian Federation;Research Computing Center, Moscow State University, Russian Federation;Moscow State University, Russian Federation
Venue:
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Year:
2013

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Abstract

The paper describes the results of an experimental study of topic models applied to the task of single-word term extraction. The experiments encompass several probabilistic and non-probabilistic topic models and demonstrate that topic information improves the quality of term extraction, as well as NMF with KL-divergence minimization is the best among the models under study.