Bibliography – Topic Model Algorithms and Application Software

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

This bibliography page concentrates on citations for algorithms and software for making topic models. Interface tools for viewing or exploring topic models are cited on the topic model visualization bibliography page. (There is some overlap in categories, however, since topic model interface tools often include embedded uses of topic-model making algorithms such as LDA and application software such as Mallet.)

Guille, Adrien, and Edmundo-Pavel Soriano-Morales. “TOM: A Library for Topic Modeling and Browsing.” In Conférence Sur l’Extraction et La Gestion Des Connaissances. Actes de La 16ème Conférence Sur l’Extraction et La Gestion Des Connaissances. Reims, France, 2016. https://hal.archives-ouvertes.fr/hal-01442868. Cite
Vulić, Ivan, Wim De Smet, Jie Tang, and Marie-Francine Moens. “Probabilistic Topic Modeling in Multilingual Settings: An Overview of Its Methodology and Applications.” Information Processing & Management 51, no. 1 (2015): 111–47. http://keg.cs.tsinghua.edu.cn/jietang/publications/ipm15-xLiTe-IvanVulic-overview-topic-model-multilingual.pdf. Cite
Blei, David M. “Probabilistic Topic Models.” Communications of the ACM 55, no. 4 (2012): 77–84. https://doi.org/10.1145/2133806.2133826. Cite
Arora, Sanjeev, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, and Michael Zhu. “A Practical Algorithm for Topic Modeling with Provable Guarantees.” ArXiv:1212.4777 [Cs, Stat], 2012. http://arxiv.org/abs/1212.4777. Cite
Wang, Chong, David Blei, and David Heckerman. “Continuous Time Dynamic Topic Models.” ArXiv:1206.3298 [Cs, Stat], 2012. http://arxiv.org/abs/1206.3298. Cite
Wu, Hao, Jiajun Bu, Chun Chen, Jianke Zhu, Lijun Zhang, Haifeng Liu, Can Wang, and Deng Cai. “Locally Discriminative Topic Modeling.” Pattern Recognition 45, no. 1 (2012): 617–25. https://doi.org/10.1016/j.patcog.2011.04.029. Cite
Ponweiser, Martin. “Latent Dirichlet Allocation in R.” Diploma Thesis, Vienna University of Economics and Business, 2012. http://epub.wu.ac.at/3558/. Cite
Boyd-Graber, Jordan, and Philip Resnik. “Holistic Sentiment Analysis across Languages: Multilingual Supervised Latent Dirichlet Allocation.” In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 45–55. Association for Computational Linguistics, 2010. http://www.aclweb.org/anthology/D10-1005. Cite
Saria, Suchi, Daphne Koller, and Anna Penn. “Discovering Shared and Individual Latent Structure in Multiple Time Series.” ArXiv:1008.2028 [Cs, Stat], 2010. http://arxiv.org/abs/1008.2028. Cite
Andrews, Mark, and Gabriella Vigliocco. “The Hidden Markov Topic Model: A Probabilistic Model of Semantic Representation.” Topics in Cognitive Science 2, no. 1 (2010): 101–13. https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1756-8765.2009.01074.x. Cite
Wallach, Hanna M., David Mimno, and Andrew McCallum. “Rethinking LDA: Why Priors Matter.” In Proceedings of the 22Nd International Conference on Neural Information Processing Systems, 1973–81. NIPS’09. USA: Curran Associates Inc., 2009. http://dl.acm.org/citation.cfm?id=2984093.2984314. 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
Boyd-Graber, Jordan, and David M. Blei. “Multilingual Topic Models for Unaligned Text.” In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, 75–82. AUAI Press, 2009. https://arxiv.org/pdf/1205.2657. Cite
Boyd-Graber, Jordan L., and David M. Blei. “Syntactic Topic Models.” In Advances in Neural Information Processing Systems, 185–92, 2009. https://papers.nips.cc/paper/3398-syntactic-topic-models.pdf. Cite
Boyd-Graber, Jordan, David Blei, and Xiaojin Zhu. “A Topic Model for Word Sense Disambiguation.” In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 1024–33, 2007. http://www.aclweb.org/anthology/D07-1109. Cite
Rosen-Zvi, Michal, Yair Weiss, and Amit Gruber. “Hidden Topic Markov Models.” In Artificial Intelligence and Statistics, 163–70, 2007. http://proceedings.mlr.press/v2/gruber07a.html. Cite
Wang, Chong, Jinggang Wang, Xing Xie, and Wei-Ying Ma. “Mining Geographic Knowledge Using Location Aware Topic Model.” In Proceedings of the 4th ACM Workshop on Geographical Information Retrieval, 65–70. GIR ’07. New York, NY, USA: ACM, 2007. https://doi.org/10.1145/1316948.1316967. Cite
Cao, L., and Li Fei-Fei. “Spatially Coherent Latent Topic Model for Concurrent Segmentation and Classification of Objects and Scenes.” In 2007 IEEE 11th International Conference on Computer Vision, 1–8, 2007. https://doi.org/10.1109/ICCV.2007.4408965. Cite
Wang, Xiaogang, and Eric Grimson. “Spatial Latent Dirichlet Allocation.” In Proceedings of the 20th International Conference on Neural Information Processing Systems, 1577–84. NIPS’07. USA: Curran Associates Inc., 2007. http://dl.acm.org/citation.cfm?id=2981562.2981760. Cite
McCallum, Andrew, and Xuerui Wang. Topics over Time: A Non-Markov Continuous-Time Model of Topical Trends, 2006. /paper/Topics-over-time%3A-a-non-Markov-continuous-time-of-Wang-McCallum/7f8abf25ca24b48450b4e535f41e2b8a87df73f5. Cite