Efficient inference of CRFs for large-scale natural language data

ACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf.

Volumn , Issue , 2009, Pages 281-284

  Jeong, Minwoo ^a   Lin, Chin Yew ^b   Lee, Gary Geunbae ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; INFERENCE ENGINES;

ACTIVE SETS; CONDITIONAL RANDOM FIELDS(CRFS); EXACT INFERENCE; INFERENCE ALGORITHM; LARGE SCALE DATA; NATURAL LANGUAGE PROCESSING; NATURAL LANGUAGES; LARGE-SCALES; PROCESSING PROBLEMS; SIMPLE++;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84859898897     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1667583.1667670     Document Type: Conference Paper

References (7)

1. T. Cohn. 2006. Efficient inference in large conditional random fields. In Proc. ECML, pages 606-613.
2. M. Jeong and G. G. Lee. 2006. Exploiting non-local features for spoken language understanding. In Proc. of COLING/ACL, pages 412-419, Sydney, Australia, July.
3. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. ICML, pages 282-289.
4. C. Lee, Y. Hwang, and M. Jang. 2007. Fine-grained named entity recognition and relation extraction for question answering. In Proc. SIGIR Poster, pages 799-800.
5. C. Pal, C. Sutton, and A. McCallum. 2006. Sparse forwardbackward using minimum divergence beams for fast training of conditional random fields. In Proc. ICASSP.
6. F. Sha and F. Pereira. 2003. Shallow parsing with conditional random fields. In Proc. of NAACL/HLT, pages 134-141.
7. C. Sutton and A. McCallum. 2006. An introduction to conditional random fields for relational learning. In Lise Getoor and Ben Taskar, editors, Introduction to Statistical Relational Learning. MIT Press, Cambridge, MA.