FACTORIE: Probabilistic programming via imperatively defined factor graphs

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 1249-1257

  McCallum, Andrew ^a   Schultz, Karl ^a   Singh, Sameer ^a  

^a The Manning College of Information and Computer Sciences   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER CIRCUITS; DOMAIN KNOWLEDGE; FORMAL LOGIC; OBJECT ORIENTED PROGRAMMING;

DECLARATIVE LANGUAGES; FACTOR GRAPHS; FIRST ORDER LOGIC; GRAPHICAL MODEL; IMPERATIVE LANGUAGES; MODEL LEARNING; POWER; PROBABILISTIC PROGRAMMING; RELATIONAL DATA; RELATIONAL MODELING;

SEMANTICS;

EID: 84863338363     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. John D. Lafferty, Andrew McCallum, and Fernando Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Int Conf on Machine Learning (ICML), 2001.
2. Charles Sutton and Andrew McCallum. An introduction to conditional random fields for relational learning. In Introduction to Statistical Relational Learning. 2007.
3. A. Bernal, K. Crammer, A. Hatzigeorgiou, and F. Pereira. Global discriminative learning for higher-accuracy computation gene prediction. In PloS Computational Biology, 2007.
4. A. Quottoni, M. Collins, and T. Darrell. Conditional random fields for object recognition. In NIPS, 2004.
5. Brian Milch. Probabilistic Models with Unknown Objects. PhD thesis, University of California, Berkeley, 2006.
6. Avi Pfeffer. IBAL: A probabilistic rational programming language. In IJCAI, pages 733-740, 2001.
7. Noah D. Goodman, Vikash K. Mansinghka, Daniel Roy, Keith Bonawitz, and Joshua B. Tenenbaum. Church: a language for generative models. In Uncertainty in Artificial Intelligence (UAI), 2008.
8. Ben Taskar, Abbeel Pieter, and Daphne Koller. Discriminative probabilistic models for relational data. In Uncertainty in Artificial Intelligence (UAI), 2002.
9. Matthew Richardson and Pedro Domingos. Markov logic networks. Machine Learning, 62(1-2), 2006.
10. David Poole. Probabilistic horn abduction and bayesian networks. Artificial Intelligence, 64, 1993.
11. Stephen Muggleton and Luc DeRaedt. Inductive logic programming theory and methods. In Journal of Logic Programming, 1994.
12. Taisuke Sato and Yoshitaka Kameya. PRISM: a language for symbolic-statistical modeling. In International Joint Conference on Artificial Intelligence (IJCAI), 1997.
13. Luc De Raedt and Kristian Kersting. Probabilistic logic learning. SIGKDD Explorations: Multi-Relational Data Mining, 2003.
14. Martin Odersky. An Overview of the Scala Programming Language (second edition). Technical Report IC/2006/001, EPFL Lausanne, Switzerland, 2006.
15. Aron Culotta and Andrew McCallum. Tractable learning and inference with high-order representations. In ICML WS on Open Problems in Statistical Relational Learning, 2006.
16. Keith A. Bonawitz. Composable Probabilistic Inference with Blaise. PhD thesis, MIT, 2008.
17. Aron Culotta. Learning and inference in weighted logic with application to natural language processing. PhD thesis, University of Massachusetts, 2008.
18. Charles Sutton and Andrew McCallum. Collective segmentation and labeling of distant entities in information extraction. Technical Report TR#04-49, University of Massachusetts, July 2004.
19. Aron Culotta, Michael Wick, and Andrew McCallum. First-order probabilistic models for coreference resolution. In NAACL: Human Language Technologies (NAACL/HLT), 2007.
20. Khashayar Rohanimanesh, Michael Wick, and Andrew McCallum. Inference and learning in large factor graphs with a rank based objective. Technical Report UM-CS-2009-08, University of Massachusetts, Amherst, 2009.
21. Hoifung Poon and Pedro Domingos. Joint inference in information extraction. In AAAI, 2007.
22. Vasin Punyakanok, Dan Roth, and Wen-tau Yih. The necessity of syntactic parsing for semantic role labeling. In International Joint Conf on Artificial Intelligence (IJCAI), pages 1117-1123, 2005.
23. Ben Wellner, Andrew McCallum, Fuchun Peng, and Michael Hay. An integrated, conditional model of information extraction and coreference with application to citation matching. In AUAI, 2004.
24. Sameer Singh, Karl Schultz, and Andrew McCallum. Bi-directional joint inference for entity resolution and segmentation using imperatively-defined factor graphs. In ECML PKDD, pages 414-429, 2009.
25. Andrew McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. A machine learning approach to building domain-specific search engines. In Int Joint Conf on Artificial Intelligence (IJCAI), 1999.
26. Hoifung Poon, Pedro Domingos, and Marc Sumner. A general method for reducing the complexity of relational inference and its application to MCMC. In AAAI, 2008.
27. Kevin Murphy and Matt Dunham. PMTK: Probabilistic modeling toolkit. In Neural Information Processing Systems (NIPS) Workshop on Probabilistic Programming, 2008.
28. John Winn and Tom Minka. Infer.NET/CSoft, 2008. http://research. microsoft.com/mlp/ml/Infer/Csoft.htm.
29. Avi Pfeffer. Figaro: An Object-Oriented Probabilistic Programming Language. Technical report, Charles River Analytics, 2009.
30. Richard Maclin and Jude W. Shavlik. Creating advice-taking reinforcement learners. Machine Learning, 22, 1996.