The paper presents an empirical study of integrating ngrams and multi-word terms into topic models, while maintaining similarities between them and words based on their component structure. First, we adapt the PLSA-SIM algorithm to the more widespread LDA model and ngrams. Then we propose a novel algorithm LDA-ITER that allows the incorporation of the most suitable ngrams into topic models. The experiments of integrating ngrams and multiword terms conducted on five text collections in different languages and domains demonstrate a significant improvement in all the metrics under consideration.
In REF an empirical study of integrating n-grams and multi-word terms into topic models is presented by the authors while maintaining similarities between them and words based on their component structure with the help of LDA-ITER algorithm by which the most suitable n-grams and multiword terms are incorporated.
Accounting ngrams and multi-word terms can improve topic models
MWE@ACL
Computer Science