Improving Topic Quality by Promoting Named Entities in Topic Modeling
In July, our R&D engineer Katherine Krasnoschok was in Melbourne, Australia to attend the ACL conference. She presented her poster on topic modelling. Her paper, co-written with Salim Jouili, indicates that involving more named entities positively influences the overall quality of topics.
News-related content has been extensively studied in both topic modeling research and named entity recognition. However, expressive power of named entities and their potential for improving the quality of discovered topics has not received much attention. In this paper, we use named entities as domain-specific terms for news-centric content and present a new weighting model for Latent Dirichlet Allocation. Our experimental results indicate that involving more named entities in topic descriptors positively influences the overall quality of topics, improving their interpretability, specificity and diversity.
Katsiaryna Krasnashchok, Salim Jouili, Improving Topic Quality by Promoting Named Entities in Topic Modeling, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Vol. 2. 2018.
Click here to access the paper.