Tandem anchoring: A multiword anchor approach for interactive topic modeling

ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)

Volumn 1, Issue , 2017, Pages 896-905

  Lund, Jeffrey ^a   Cook, Connor ^c   Seppi, Kevin ^c   Boyd Graber, Jordan ^b  

^a BRIGHAM YOUNG UNIVERSITY   (United States)

^b University of Colorado   (United States)

^c NONE

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; LINGUISTICS; SEMANTICS;

INTERACTIVE APPROACH; LARGE DATASETS; MULTI-WORD; PRIOR KNOWLEDGE; SAMPLING-BASED; SINGLE WORDS; TOPIC MODEL; TOPIC MODELING;

ANCHORS;

EID: 85040918264     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/P17-1083     Document Type: Conference Paper

References (27)

1. Loulwah AlSumait, Daniel Barbará, James Gentle, and Carlotta Domeniconi. 2009. Topic significance ranking of LDA generative models. In Proceedings of European Conference of Machine Learning.
2. David Andrzejewski, Xiaojin Zhu, and Mark Craven. 2009. Incorporating domain knowledge into topic modeling via Dirichlet forest priors. In Proceedings of the International Conference of Machine Learning.
3. Sanjeev Arora, Rong Ge, Yonatan Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, and Michael Zhu. 2013. A practical algorithm for topic modeling with provable guarantees. In Pro-ceedingsoftheInternationalConferenceofMachine Learning.
4. Sanjeev Arora, Rong Ge, Ravindran Kannan, and Ankur Moitra. 2012a. Computing a nonnegative matrix factorization-provably. In Proceedings of the forty-fourth annual ACM symposium on Theory of computing.
5. Sanjeev Arora, Rong Ge, and Ankur Moitra. 2012b. Learning topic models-going beyond svd. In Fifty-Third IEEE Annual Symposium on Foundations of Computer Science.
6. David M. Blei, Andrew Ng, and Michael Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3:993-1022.
7. Jaegul Choo, Changhyun Lee, Chandan K Reddy, and Heejung Park. 2013. Utopian: User-driven topic modeling based on interactive nonnegative matrix factorization. Visualization and Computer Graphics, IEEE Transactions on 19(12):1992-2001.
8. Jason Chuang, Christopher D Manning, and Jeffrey Heer. 2012. Termite: Visualization techniques for assessing textual topic models. In Proceedings of the International Working Conference on Advanced Visual Interfaces.
9. Kristin A. Cook and James J. Thomas. 2005. Illuminating the path: The research and development agenda for visual analytics. Technical report, Pacific Northwest National Laboratory (PNNL), Richland, WA (US).
10. Ralph D'Agostino and Egon S Pearson. 1973. Tests for departure from normality. empirical results for the distributions of b2 and b1. Biometrika 60(3):613-622.
11. Matthew J Gardner, Joshua Lutes, Jeff Lund, Josh Hansen, Dan Walker, Eric Ringger, and Kevin Seppi. 2010. The topic browser: An interactive tool for browsing topic models. In NIPS Workshop on Challenges of Data Visualization.
12. Harold Stanley Heaps. 1978. Information retrieval: Computational and theoretical aspects, Academic Press, Inc., pages 206-208.
13. Matthew Hoffman, Francis R Bach, and David M Blei. 2010. Online learning for latent dirichlet allocation. In advances in neural information processing systems.
14. Matthew D Hoffman, David M Blei, Chong Wang, and John William Paisley. 2013. Stochastic variational inference. Journal of Machine Learning Research 14(1):1303-1347.
15. Yuening Hu and Jordan Boyd-Graber. 2012. Efficient tree-based topic modeling. In Proceedings of the Association for Computational Linguistics.
16. Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith. 2014. Interactive topic modeling. Machine Learning 95(3):423-469.
17. Moontae Lee and David Mimno. 2014. Low-dimensional embeddings for interpretable anchor-based topic inference. In Proceedings of Empirical Methods in Natural Language Processing.
18. Marina Meila. 2003. Comparing clusterings by the variation of information. In Learning theory and kernel machines.
19. David Newman, Jey Han Lau, Karl Grieser, and Timothy Baldwin. 2010. Automatic evaluation of topic coherence. In Proceedings of the Association for Computational Linguistics.
20. Thang Nguyen, Jordan Boyd-Graber, Jeffrey Lund, Kevin Seppi, and Eric Ringger. 2015. Is your anchor going up or down? Fast and accurate supervised topic models. In Conference of the North American Chapter of the Association for Computational Linguistics.
21. Thang Nguyen, Yuening Hu, and Jordan L Boyd-Graber. 2014. Anchors regularized: Adding robustness and extensibility to scalable topic-modeling algorithms. In Proceedings of the Association for Computational Linguistics.
22. Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, and Padhraic Smyth. 2004. The author-topic model for authors and documents. In Proceedings of Uncertainty in Artificial Intelligence.
23. Timothy Rubin, America Chambers, Padhraic Smyth, and Mark Steyvers. 2012. Statistical topic models for multi-label document classification. Machine Learning 1(88):157-208.
24. Ivan Titov and Ryan T McDonald. 2008. A joint model of text and aspect ratings for sentiment summarization. In Proceedings of the Association for Computational Linguistics.
25. Xing Wei and W Bruce Croft. 2006. LDA-based document models for ad-hoc retrieval. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval.
26. Frank Wilcoxon. 1945. Individual comparisons by ranking methods. Biometrics bulletin 1(6):80-83.
27. Ka Yee Yeung and Walter L Ruzzo. 2001. Details of the adjusted rand index and clustering algorithms, supplement to the paper an empirical study on principal component analysis for clustering gene expression data. Bioinformatics 17(9):763-774.