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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:730 - 25 self
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BibTeX

@INPROCEEDINGS{Finkel05incorporatingnon-local,
    author = {Jenny Rose Finkel and Trond Grenager and Christopher Manning},
    title = {Incorporating non-local information into information extraction systems by Gibbs sampling},
    booktitle = {IN ACL},
    year = {2005},
    pages = {363--370},
    publisher = {}
}

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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.

Keyphrases

gibbs sampling    non-local information    information extraction system    label consistency    crf-based information extraction system    state-of-the-art system    language use    simple monte carlo method    extraction template consistency constraint    long distance structure    long-distance dependency model    simulated annealing    factored probabilistic model    tractable inference    approximate inference    sequence model    dynamic programming    technique result    information extraction task    error reduction    non-local structure    local feature   

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