Summarizing topical content with word frequency and exclusivity

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 1, Issue , 2012, Pages 201-208

  Bischof, Jonathan M ^a   Airoldi, Edoardo M ^a  

^a HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HIERARCHICAL STRUCTURES; MONTE CARLO; TEXT ANALYSIS; WORD FREQUENCIES;

ARTIFICIAL INTELLIGENCE; SOFTWARE ENGINEERING;

LEARNING SYSTEMS;

EID: 84867113614     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Adams, R. et al. Tree-structured stick breaking for hierarchical data. NIPS 2010.
2. Airoldi, E. M., et al. Who wrote Ronald Reagan's radio addresses? Bayesian Analysis, 1(2):289-320, 2006.
3. Ashburner, M. et al. Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nature Genetics, 25(1):25-29, 2000.
4. Blei., D. Introduction to probabilistic topic models. Communications of the ACM, 2012. In press.
5. Blei, David et al. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003b.
6. Canny, J. GAP: A Factor Model for Discrete Data. SIGIR, 2004.
7. Chang, J. et al. Reading tea leaves: How humans interpret topic models. NIPS, 2009.
8. Eisenstein, J. et al. Sparse Additive Generative Models of Text. ICML, 2011.
9. Hu, Y. et al. Interactive Topic Modeling. ACL, 2011.
10. Kanehisa, M. and Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28(1): 27-30, 2000.
11. Lewis, D. et al. RCV1: A New Benchmark Collection for Text Categorization Research. Journal of Machine Learning Research, 5:361-397, 2004.
12. McCallum, A. et al. Improving text classification by shrinkage in a hierarchy of classes. ICML, 1998.
13. Mimno, D. et al. Optimizing Semantic Coherence in Topic Models. EMNLP, 2011.
14. Mosteller, F. and Wallace, D.L. Applied Bayesian and Classical Inference: The Case of "The Federalist" Papers. Springer-Verlag, 1984.
15. Neal, R. MCMC using Hamiltonian dynamics. In Brooks, S., et al. (eds.), Handbook of Markov Chain Monte Carlo. Chapman & Hall / CRC Press, 2011.
16. Perotte, A. et al. Hierarchically Supervised Latent Dirichlet Allocation. NIPS, 2012.
17. Ramage, D. et al. Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. EMNLP, 2009.
18. Wallach, H. et al. Rethinking LDA: Why Priors Matter. NIPS, 2009.
19. Zhu, J. and Xing, E. P. Sparse Topical Coding. UAI, 2011.