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

Open access
Finkel, Jenny Rose, Grenager, Trond, Manning, Christopher·2005·Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05

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.

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Metadata

Authors
Finkel, J. R., Grenager, T., & Manning, C.
Venue
Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05
Published
2005
Pages
363-370
OpenAlex
W2096765155
Cited by
3,008
References
2
FWCI
18.60
Type
conference paper
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Incorporating non-local information into information extraction systems by Gibbs sampling. https://app.cochat.ai/papers/10.3115/1219840.1219885
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Finkel, J. R., Grenager, T., & Manning, C. (2005). Incorporating non-local information into information extraction systems by Gibbs sampling. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05. https://doi.org/10.3115/1219840.1219885

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