Probabilistic joint models incorporating logic and learning via structured variational approximation for information extraction

Knowledge and Information Systems

Volumn 32, Issue 2, 2012, Pages 415-444

  Yu, Xiaofeng ^a   Lam, Wai ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Bi directional Metropolis Hastings sampling; First order logic; Joint information extraction; Probabilistic graphical models; Structured variational approximation

Indexed keywords

FORMAL LOGIC; INFERENCE ENGINES; INFORMATION RETRIEVAL; PARAMETER ESTIMATION; PIPELINES; PROBABILITY DISTRIBUTIONS; UNCERTAINTY ANALYSIS;

FIRST ORDER LOGIC; JOINT INFORMATION; METROPOLIS-HASTINGS SAMPLINGS; PROBABILISTIC GRAPHICAL MODELS; STRUCTURED VARIATIONAL APPROXIMATIONS;

COMPUTER CIRCUITS;

EID: 84864293378     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-011-0455-8     Document Type: Article

References (46)

1. Cowie J, Lehnert W (1996) Information extraction. Commun ACM 39: 80-91.
2. Yu X (2007) Chinese named entity recognition with cascaded hybrid model. In: Proceedings of HLT/NAACL-07, Rochester, New York, pp 197-200.
3. Yu X, Lam W, Chen B (2009) An integrated discriminative probabilistic approach to information extraction. In: Proceedings of CIKM-09, Hong Kong, China, pp 325-334.
4. Song M, Rudniy A (2010) Detecting duplicate biological entities using Markov random field-based edit distance. Knowl Inf Syst 25(2): 371-387.
5. Culotta A, McCallum A, Betz J (2006) Integrating probabilistic extraction models and data mining to discover relations and patterns in text. In: Proceedings of HLT/NAACL-06, New York, pp 296-303.
6. Yu X, Lam W (2008) An integrated probabilistic and logic approach to encyclopedia relation extraction with multiple features. In: Proceedings of COLING-08, Manchester, United Kingdom, pp 1065-1072.
7. Zhu J, Nie Z, Liu X, Zhang B, Wen J-R (2009) Statsnowball: a statistical approach to extracting entity relationships. In: Proceedings of WWW-09, Madrid, Spain, pp 101-110.
8. Patnaik D, Laxman S, Ramakrishnan N (2010) Discovering excitatory relationships using dynamic Bayesian networks. Knowl Inf Syst 29(2): 273-303.
9. Reichartz F, Korte H, Paass G (2010) Semantic relation extraction with kernels over typed dependency trees. In: Proceedings of KDD-10, New York, pp 773-782.
10. Wellner B, McCallum A, Peng F, Hay M (2004) An integrated, conditional model of information extraction and coreference with application to citation matching. In: Proceedings of UAI-04, Banff, Canada, pp 593-601.
11. Poon H, Domingos P (2007) Joint inference in information extraction. In: Proceedings of AAAI-07, Vancouver, British Columbia, Canada, pp 913-918.
12. Finkel JR, Manning CD, Ng AY (2006) Solving the problem of cascading errors: approximate Bayesian inference for linguistic annotation pipelines. In: Proceedings of EMNLP-06, Sydney, Australia, pp 618-626.
13. Hollingshead K, Roark B (2007) Pipeline iteration. In: Proceedings of ACL-07, Prague, Czech Republic, pp 952-959.
14. Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICML-01, pp 282-289.
15. Bunescu R, Mooney RJ (2004) Collective information extraction with relational Markov networks. In: Proceedings of ACL-04, Barcelona, Spain.
16. Zhu J, Zhang B, Nie Z, Wen J-R, Hon H-W (2007) Webpage understanding: an integrated approach. In: Proceedings of KDD-07, San Jose, California, USA, pp 903-912.
17. Zhu J, Nie Z, Zhang B, Wen J-R (2007) Dynamic hierarchical Markov random fields and their application to Web data extraction. In: Proceedings of ICML-07, Corvalis, Oregon, pp 1175-1182.
18. Yang C, Cao Y, Nie Z, Zhou J, Wen J-R (2010) Closing the loop in webpage understanding. IEEE Trans Knowl Data Eng (forthcoming).
19. Luo P, Lin F, Xiong Y, Zhao Y, Shi Z (2009) Towards combining Web classification and Web information extraction: a case study. In: Proceedings of KDD-09, Paris, France, pp 1235-1244.
20. Pascot D, Bouslama F, Mellouli S (2011) Architecturing large integrated complex information systems: an application to healthcare. Knowl Inf Syst 27(1): 115-140.
21. Sutton C, McCallum A (2006) An introduction to conditional random fields for relational learning. In: Getoor L, Taskar B Introduction to statistical relational learning. MIT Press, Cambridge, MA, USA.
22. Richardson M, Domingos P (2006) Markov logic networks. Mach Learn 62(1-2): 107-136.
23. Sarawagi S, Cohen WW (2004) Semi-Markov conditional random fields for information extraction. In: Proceedings of NIPS-04.
24. Saul LK, Jordan MI (1996) Exploiting tractable substructures in intractable networks. In: Proceedings of NIPS-96, Cambridge, MA, pp 486-492.
25. Wiegerinck W (2000) Variational approximations between mean field theory and the junction tree algorithm. In: Proceedings of UAI-2000, San Francisco, CA, pp 626-633.
26. Wainwright MJ, Jordan MI (2008) Graphical models, exponential families, and variational inference. Found Trends Mach Learn 1: 1-305.
27. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equations of state calculations by fast computing machines. J Chem Phys 21(6): 1087-1092.
28. Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1): 97-109.
29. Carbonetto P, Kisyński J, Freitas OD, Poole D (2005) Nonparametric Bayesian logic. In: Proceedings of UAI-05, pp 85-93.
30. Culotta A, Wick M, McCallum A (2007) First-order probabilistic models for coreference resolution. In: Proceedings of HLT/NAACL-07, pp 81-88.
31. Taskar B, Abbeel P, Koller D (2002) Discriminative probabilistic models for relational data. In: Proceedings of UAI-02, pp 485-492.
32. Zhu J, Nie Z, Wen J-R, Zhang B, Ma W-Y (2006) Simultaneous record detection and attribute labeling in Web data extraction. In: Proceedings of KDD-06, Philadelphia, Pennsylvania, USA, pp 494-503.
33. Liao L, Fox D, Kautz H (2007) Extracting places and activities from GPS traces using hierarchical conditional random fields. Int J Robotics Res 26: 119-134.
34. Hu B (2010) WiKi'mantics: interpreting ontologies with Wikipedia. Knowl Inf Syst 25(3): 445-472.
35. Genesereth MR, Nislsson NJ (1987) Logical foundations of artificial intelligence. Morgan Kaufmann Publishers Inc., San Mateo.
36. Zhu J, Lao N, Xing EP (2010) Grafting-light: fast, incremental feature selection and structure learning of Markov random fields. In: Proceedings of KDD-10, New York, pp 303-312.
37. Kschischang FR, Frey BJ, Loeliger H-A (2001) Factor graphs and the sum-product algorithm. IEEE Trans Inf Theory 47(2): 498-519.
38. Besag J (1974) Spatial interaction and the statistical analysis of lattice systems. J Royal Stat Soc 36: 192-236.
39. Jordan MI, Ghahramani Z, Jaakkola TS, Saul LK (1999) An introduction to variational methods for graphical methods. Mach Learn 37: 183-233.
40. Jaakkola TS (2000) Tutorial on variational approximation methods. In: Advanced mean field methods: theory and practice. MIT Press, Cambrige, pp 129-159.
41. Xing EP, Jordan MI, Russell S (2003) A generalized mean field algorithm for variational inference in exponential families. In: Proceedings of UAI-03, pp 583-591.
42. Xing EP, Jordan MI, Russell S (2004) Graph partition strategies for generalized mean field inference. In: Proceedings of UAI-04, pp 602-610.
43. Liu DC, Nocedal J (1989) On the limited memory BFGS method for large scale optimization. Math Program 45: 503-528.
44. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220: 671-680.
45. Nguyen DPT, Matsuo Y, Ishizuka M (2007) Relation extraction from Wikipedia using subtree mining. In: Proceedings of AAAI-07, Vancouver, British Columbia, Canada, pp 1414-1420.
46. Singla P, Domingos P (2006) Entity resolution with Markov logic. In: Proceedings of ICDM-06, pp 572-582.