Monte Carlo MCMC: Efficient inference by approximate sampling

EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference

Volumn , Issue , 2012, Pages 1104-1113

  Singh, Sameer ^a   Wick, Michael ^a   McCallum, Andrew ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE INFERENCE; CONDITIONAL RANDOM FIELD; GRAPHICAL MODEL; MAP INFERENCES; MCMC SAMPLING; RELATION EXTRACTION; STATE OF THE ART; TRANSITION PROBABILITIES;

SAMPLING; TREES (MATHEMATICS);

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84883422718     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Amit Bagga and Breck Baldwin. 1998. Algorithms for scoring coreference chains. In International Conference on Language Resources and Evaluation (LREC) Workshop on Linguistics Coreference, pages 563-566.
2. D. Bertsimas and J. Tsitsiklis. 1993. Simulated annealing. Statistical Science, pages 10-15.
3. Xavier Carreras. 2007. Experiments with a higher-order projective dependency parser. In Proceedings of the CoNLL Shared Task Session of EMNLP-CoNLL 2007, pages 957-961.
4. J. Andrés Christen and Colin Fox. 2005. Markov chain monte carlo using an approximation. Journal of Computational and Graphical Statistics, 14(4):pp. 795-810.
5. James Coughlan and Huiying Shen. 2007. Dynamic quantization for belief propagation in sparse spaces. Computer Vision and Image Understanding, 106:47-58, April.
6. Aron Culotta, Michael Wick, and Andrew McCallum. 2007. First-order probabilistic models for coreference resolution. In North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT).
7. Joseph Gonzalez, Yucheng Low, and Carlos Guestrin. 2009. Residual splash for optimally parallelizing belief propagation. In Artificial Intelligence and Statistics (AISTATS).
8. Joseph Gonzalez, Yucheng Low, Arthur Gretton, and Carlos Guestrin. 2011. Parallel gibbs sampling: From colored fields to thin junction trees. In Artificial Intelligence and Statistics (AIS-TATS), Ft. Lauderdale, FL, May.
9. Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S. Weld. 2011. Knowledge-based weak supervision for information extraction of overlapping relations. In Annual Meeting of the Association for Computational Linguistics (ACL), pages 541-550, Portland, Oregon, USA, June. Association for Computational Linguistics.
10. Geoff Hulten and Pedro Domingos. 2002. Mining complex models from arbitrarily large databases in constant time. In International Conference on Knowledge Discovery and Data Mining (KDD), pages 525-531, New York, NY, USA. ACM.
11. Stanley Kok and Pedro Domingos. 2005. Learning the structure of markov logic networks. In International Conference on Machine Learning (ICML), pages 441-448, New York, NY, USA. ACM.
12. Frank R. Kschischang, Brendan J. Frey, and Hans Andrea Loeliger. 2001. Factor graphs and the sum-product algorithm. IEEE Transactions of Information Theory, 47(2):498-519, Feb.
13. John D. Lafferty, Andrew McCallum, and Fernando Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In International Conference on Machine Learning (ICML).
14. Jure Leskovec and Christos Faloutsos. 2006. Sampling from large graphs. In International Conference on Knowledge Discovery and Data Mining (KDD), pages 631-636, New York, NY, USA. ACM.
15. Andrew McCallum and Ben Wellner. 2004. Conditional models of identity uncertainty with application to noun coreference. In Neural Information Processing Systems (NIPS).
16. Andrew McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. 1999. A machine learning approach to building domain-specific search engines. In International Joint Conference on Artificial Intelligence (IJCAI).
17. Andrew McCallum, Karl Schultz, and Sameer Singh. 2009. FACTORIE: Probabilistic programming via imperatively defined factor graphs. In Neural Information Processing Systems (NIPS).
18. Iain Murray and Zoubin Ghahramani. 2004. Bayesian learning in undirected graphical models: Approximate MCMC algorithms. In Uncertainty in Artificial Intelligence (UAI).
19. H. Pasula, B. Marthi, B. Milch, S. Russell, and I. Shpitser. 2003. Identity uncertainty and citation matching. In Neural Information Processing Systems (NIPS).
20. Hoifung Poon and Pedro Domingos. 2006. Sound and efficient inference with probabilistic and deterministic dependencies. In AAAI Conference on Artificial Intelligence.
21. Hoifung Poon and Pedro Domingos. 2007. Joint inference in information extraction. In AAAI Conference on Artificial Intelligence, pages 913-918.
22. Hoifung Poon, Pedro Domingos, and Marc Sumner. 2008. A general method for reducing the complexity of relational inference and its application to MCMC. In AAAI Conference on Artificial Intelligence.
23. Matthew Richardson and Pedro Domingos. 2006. Markov logic networks. Machine Learning, 62(1-2):107-136.
24. Sebastian Riedel and David A. Smith. 2010. Relaxed marginal inference and its application to dependency parsing. In North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT), pages 760-768.
25. Sameer Singh, Karl Schultz, and Andrew McCallum. 2009. Bi-directional joint inference for entity resolution and segmentation using imperatively-defined factor graphs. In Machine Learning and Knowledge Discovery in Databases (Lecture Notes in Computer Science) and European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pages 414-429.
26. Sameer Singh, Michael L. Wick, and Andrew McCallum. 2010. Distantly labeling data for large scale cross-document coreference. Computing Research Repository (CoRR), abs/1005.4298.
27. Sameer Singh, Amarnag Subramanya, Fernando Pereira, and Andrew McCallum. 2011. Large-scale cross-document coreference using distributed inference and hierarchical models. In Association for Computational Linguistics: Human Language Technologies (ACL HLT).
28. Charles Sutton and Andrew McCallum. 2004. Collective segmentation and labeling of distant entities in information extraction. Technical Report TR#04-49, University of Massachusetts, July.
29. Charles Sutton and Andrew McCallum. 2007. Improved dynamic schedules for belief propagation. In Uncertainty in Artificial Intelligence (UAI).
30. Michael Wick, Aron Culotta, Khasha-yar Rohanimanesh, and Andrew McCallum. 2009. An entity-based model for coreference resolution. In SIAM International Conference on Data Mining (SDM).
31. Michael Wick, Andrew McCallum, and Gerome Miklau. 2010. Scalable probabilistic databases with factor graphs and mcmc. International Conference on Very Large Databases (VLDB), 3:794-804, September.
32. Michael Wick, Khashayar Rohani-manesh, Kedar Bellare, Aron Culotta, and Andrew McCallum. 2011. Samplerank: Training factor graphs with atomic gradients. In International Conference on Machine Learning (ICML).
33. Limin Yao, Sebastian Riedel, and Andrew McCallum. 2010. Collective cross-document relation extraction without labelled data. In Empirical Methods in Natural Language Processing (EMNLP).