Nguyen, Viet-An & Boyd-Graber, Jordan & Resnik, Ps & Miler, Kristina. (2015). Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress. 1438-1448. 10.3115/v1/P15-1139. 

University of Michigan - Ann Arbor

Research papers, articles and examples about the topic model in NLP

Topic Model References

https://en.wikipedia.org/wiki/Topic_model

Topic Model Definition

Yang, W., Boyd-Graber, J.L., & Resnik, P. (2017). Adapting Topic Models using Lexical Associations with Tree Priors. EMNLP. Retrieved from https://www.aclweb.org/anthology/D17-1203.pdf

Adapting Topic Models using Lexical Associations with Tree Priors

Oh, Byungkook & Seo, Seungmin & Shin, Cheolheon & Jo, Eunju & Lee, Kyong-Ho. (2019). Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information. 2273-2283. 10.18653/v1/D19-1232. 

Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information

Lee, Moontae & Cho, Sungjun & Bindel, David & Mimno, David. (2019). Practical Correlated Topic Modeling and Analysis via the Rectified Anchor Word Algorithm. 4992-5002. 10.18653/v1/D19-1504. 

Practical Correlated Topic Modeling and Analysis via the Rectified Anchor Word Algorithm

Das, Rajarshi & Zaheer, Manzil & Dyer, Chris. (2015). Gaussian LDA for Topic Models with Word Embeddings. 795-804. 10.3115/v1/P15-1077. 

Gaussian LDA for Topic Models with Word Embeddings

Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress

Lund, Jeffrey & Cook, Connor & Seppi, Kevin & Boyd-Graber, Jordan. (2017). Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling. 896-905. 10.18653/v1/P17-1083. 

Learn Before

Related