Incorporating non-local information into information extraction systems by gibbs sampling

Incorporating non-local information into information extraction systems by gibbs sampling (2005)

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by Jenny Rose Finkel , Trond Grenager , Christopher Manning

Venue:	In ACL
Citations:	192 - 15 self

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Datum	Value	Source
TITLE	Incorporating non-local information into information extraction systems by gibbs sampling	INFERENCE
AUTHOR NAME	Jenny Rose Finkel	SVM HeaderParse 0.2
AUTHOR AFFIL	Computer Science Department; Stanford University	SVM HeaderParse 0.2
AUTHOR ADDR	Stanford, CA 94305	SVM HeaderParse 0.2
AUTHOR NAME	Trond Grenager	SVM HeaderParse 0.2
AUTHOR AFFIL	Computer Science Department; Stanford University	SVM HeaderParse 0.2
AUTHOR ADDR	Stanford, CA 94305	SVM HeaderParse 0.2
AUTHOR NAME	Christopher Manning	SVM HeaderParse 0.2
AUTHOR AFFIL	Computer Science Department; Stanford University	SVM HeaderParse 0.2
AUTHOR ADDR	Stanford, CA 94305	SVM HeaderParse 0.2
ABSTRACT	Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9 % over state-of-the-art systems on two established information extraction tasks. 1	SVM HeaderParse 0.2
YEAR	2005	INFERENCE
VENUE	In ACL	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	363--370	INFERENCE
CITATIONS	21 found	ParsCit 1.0