Proceedings Article10.1145/1102351.1102444
Integer linear programming inference for conditional random fields
Dan Roth,Wen-tau Yih +1 more
- 07 Aug 2005
- pp 736-743
TL;DR: A novel inference procedure based on integer linear programming (ILP) and extends CRF models to naturally and efficiently support general constraint structures is proposed and Experimental evidence is supplied in the context of an important NLP problem, semantic role labeling.
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Abstract: Inference in Conditional Random Fields and Hidden Markov Models is done using the Viterbi algorithm, an efficient dynamic programming algorithm. In many cases, general (non-local and non-sequential) constraints may exist over the output sequence, but cannot be incorporated and exploited in a natural way by this inference procedure. This paper proposes a novel inference procedure based on integer linear programming (ILP) and extends CRF models to naturally and efficiently support general constraint structures. For sequential constraints, this procedure reduces to simple linear programming as the inference process. Experimental evidence is supplied in the context of an important NLP problem, semantic role labeling.
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Citations
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An Introduction to Conditional Random Fields for Relational Learning
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- 01 Jan 2007
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An Introduction to Conditional Random Fields
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- 10 Aug 2012
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Solving General Arithmetic Word Problems
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References
•Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
John Lafferty,Andrew McCallum,Fernando Pereira +2 more
- 28 Jun 2001
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
•Book
Integer programming
George L. Nemhauser,Laurence A. Wolsey +1 more
- 01 Jan 1972
TL;DR: The principles of integer programming are directed toward finding solutions to problems from the fields of economic planning, engineering design, and combinatorial optimization as mentioned in this paper, which is a standard of graduate-level courses since 1972.
4.6K
Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms
Michael Collins
- 06 Jul 2002
TL;DR: Experimental results on part-of-speech tagging and base noun phrase chunking are given, in both cases showing improvements over results for a maximum-entropy tagger.
Shallow parsing with conditional random fields
Fei Sha,Fernando Pereira +1 more
- 27 May 2003
TL;DR: This work shows how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model.
1.5K