A probabilistic model for canonicalizing named entity mentions

50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference

Volumn 1, Issue , 2012, Pages 685-693

  Yogatama, Dani ^a   Sim, Yanchuan ^a   Smith, Noah A ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGGLOMERATIVE CLUSTERING; BAYESIAN INFERENCE; ENTITY CONTEXTS; FIRST-ORDER DEPENDENCY; POLITICAL BLOGS; PROBABILISTIC MODELS; SURFACE FEATURE; TRANSDUCTIVE LEARNING;

BAYESIAN NETWORKS; DATA PROCESSING; INFERENCE ENGINES;

COMPUTATIONAL LINGUISTICS;

EID: 84878199388     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. G. Andrew and J. Gao. 2007. Scalable training of L1- regularized log-linear models. In Proc. of ICML.
2. N. Chambers and D. Jurafsky. 2011. Template-based information extraction without the templates. In Proc. of ACL-HLT.
3. E. Charniak. 2001. Unsupervised learning of name structure from coreference data. In Proc. of NAACL.
4. J. Eisenstein and E. P. Xing. 2010. The CMU 2008 political blog corpus. Technical report, Carnegie Mellon University.
5. J. Eisenstein, T. Yano, W. W. Cohen, N. A. Smith, and E. P. Xing. 2011. Structured databases of named entities from Bayesian nonparametrics. In Proc. of EMNLP Workshop on Unsupervised Learning in NLP.
6. M. Elsner, E. Charniak, and M. Johnson. 2009. Structured generative models for unsupervised namedentity clustering. In Proc. of NAACL-HLT.
7. J. R. Finkel, T. Grenager, and C. Manning. 2005. Incorporating non-local information into information extraction systems by Gibbs sampling. In Proc. of ACL.
8. A. Haghighi and D. Klein. 2007. Unsupervised coreference resolution in a nonparametric Bayesian model. In Proc. of ACL.
9. A. Haghighi and D. Klein. 2010. An entity-level approach to information extraction. In Proc. of ACL Short Papers.
10. C. S. Jensen, U. Kjaerulff, and A. Kong. 1995. Blocking Gibbs sampling in very large probabilistic expert system. International Journal of Human-Computer Studies, 42(6):647-666.
11. R. Jonker and A. Volgenant. 1987. A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing, 38(4):325-340.
12. X. Li, P. Morie, and D. Roth. 2004. Identification and tracing of ambiguous names: discriminative and generative approaches. In Proc. of AAAI.
13. D. C. Liu and J. Nocedal. 1989. On the limited memory BFGS method for large scale optimization. Mathematical Programming B, 45(3):503-528.
14. D. Mochihashi, T. Yamada, and N. Ueda. 2009. Bayesian unsupervised word segmentation with nested Pitman-Yor language modeling. In Proc. of ACLIJCNLP.
15. C. Morris. 1983. Parametric empirical Bayes inference: Theory and applications. Journal of the American Statistical Association, 78(381):47-65.
16. H. Poon and P. Domingos. 2008. Joint unsupervised coreference resolution with Markov logic. In Proc. of EMNLP.
17. S. Singh, A. Subramanya, F. Pereira, and A. McCallum. 2011. Large-scale cross-document coreference using distributed inference and hierarchical models. In Proc. of ACL-HLT.
18. N. A. Smith and J. Eisner. 2005. Contrastive estimation: training log-linear models on unlabeled data. In Proc. of ACL.
19. R. Tibshirani. 1996. Regression shrinkage and selection via the lasso. Journal of Royal Statistical Society B, 58(1):267-288.
20. G. C. G. Wei and M. A. Tanner. 1990. A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. Journal of the American Statistical Association, 85(411):699-704.