Topic Models: Accounting Component Structure of Bigrams
Michael Nokel, Natalia Loukachevitch · DSpace repository (University of Tartu) · 2015
The paper describes the results of an empirical study of integrating bigram collocations and similarities between them and unigrams into topic models.First of all, we propose a novel algorithm PLSA-SIM that is a modification of the original algorithm PLSA.It incorporates bigrams and maintains relationships between unigrams and bigrams based on their component structure.Then we analyze a variety of word association measures in order to integrate top-ranked bigrams into topic models.All experiments were conducted on four text collections of different domains and languages.The experiments distinguish a subgroup of tested measures that produce top-ranked bigrams, which demonstrate significant improvement of topic models quality for all collections, when integrated into PLSA-SIM algorithm.