Bibliography – Topic Model Labeling

Selected DH research and resources bearing on, or utilized by, the WE1S project.
(all) Distant Reading | Cultural Analytics | | Sociocultural Approaches | Topic Modeling in DH | Non-consumptive Use


Yoo, Alex. Automatic Topic Labeling in 2018: History and Trends, 2018. https://medium.com/datadriveninvestor/automatic-topic-labeling-in-2018-history-and-trends-29c128cec17. Cite
Bhatia, Shraey, Jey Han Lau, and Timothy Baldwin. “Automatic Labelling of Topics with Neural Embeddings.” ArXiv:1612.05340 [Cs], 2016. http://arxiv.org/abs/1612.05340. Cite
Boyd-Graber, Jordan, David Mimno, and David Newman. “Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements.” Handbook of Mixed Membership Models and Their Applications, 2014. https://doi.org/10.1201/b17520-21. Cite
Aletras, Nikolaos, and Mark Stevenson. “Labelling Topics Using Unsupervised Graph-Based Methods.” In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 631–36. Baltimore, Maryland: Association for Computational Linguistics, 2014. https://doi.org/10.3115/v1/P14-2103. Cite
Lau, Jey Han, Karl Grieser, David Newman, and Timothy Baldwin. “Automatic Labelling of Topic Models.” In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1, 1536–45. HLT ’11. Stroudsburg, PA, USA: Association for Computational Linguistics, 2011. http://dl.acm.org/citation.cfm?id=2002472.2002658. Cite
Ramage, Daniel, David Hall, Ramesh Nallapati, and Christopher D. Manning. “Labeled LDA: A Supervised Topic Model for Credit Attribution in Multi-Labeled Corpora.” In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1, 248–56. EMNLP ’09. Stroudsburg, PA, USA: Association for Computational Linguistics, 2009. http://dl.acm.org/citation.cfm?id=1699510.1699543. Cite
Mei, Qiaozhu, Xuehua Shen, and ChengXiang Zhai. “Automatic Labeling of Multinomial Topic Models.” In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’07, 490. San Jose, California, USA: ACM Press, 2007. https://doi.org/10.1145/1281192.1281246. Cite