Increasing Representative Ability for Topic Representation
Rong Yan, Ailing Tang, Ziyi Zhang · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2022
As for standard topic model, such as LDA (Latent Dirichlet Allocation), each topic is generally depicted by a weighted word set, where the high-ranked words are deemed more representative.Meanwhile, the probability of each word is considered as the ability to represent the semantic contribution for the topic.However, few efforts are focused on enhancing the representative ability of the topic to support fine grained topic representation.In this paper, we propose a Word Topic Ware (WTW) model to take word inherent diversity characteristic into consideration, in order to screen out and enhance the more representative words for topic representation.Experimental results on three large datasets show that our proposed method can increase the representative ability for topic representation.In addition, our work will positively affect improving the quality of topic content analysis.