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 topranked bigrams, which demonstrate significant improvement of topic models quality for all collections, when integrated into PLSA-SIM algorithm.
REF extend the first work and propose the PLSA-SIM algorithm, which integrates top-ranked bigrams and maintains the relationships between bigrams sharing the same words.
A Method of Accounting Bigrams in Topic Models
MWE@NAACL-HLT
Computer Science