More Efficient Topic Modelling Through a Noun Only Approach
Fiona R. Martin, Mark S. Johnson · 2015
This study compared three topic models trained on three versions of a news cor-pus. The first model was generated from the raw news corpus, the second was gen-erated from the lemmatised version of the news corpus, and the third model was generated from the lemmatised news cor-pus reduced to nouns only. We found that the removing all words except nouns improved the topics ’ semantic coherence. Using the measures developed by Lau et al (2014), the average observed topic coher-ence improved 6 % and the average word intrusion detection improved 8 % for the noun only corpus, compared to modelling the raw corpus. Similar improvements on these measures were obtained by simply lemmatising the news corpus, however, the model training times are faster when reducing the articles to the nouns only. 1