Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Constraint-based sentence compression an integer programming approach
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Incremental integer linear programming for non-projective dependency parsing
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
The necessity of syntactic parsing for semantic role labeling
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Concise integer linear programming formulations for dependency parsing
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Semantic Role Labeling
On dual decomposition and linear programming relaxations for natural language processing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Exact decoding of phrase-based translation models through Lagrangian relaxation
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Applying piecewise approximation in perceptron training of conditional random fields
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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This paper deals with the problem of predicting structures in the context of NLP. Typically, in structured prediction, an inference procedure is applied to each example independently of the others. In this paper, we seek to optimize the time complexity of inference over entire datasets, rather than individual examples. By considering the general inference representation provided by integer linear programs, we propose three exact inference theorems which allow us to re-use earlier solutions for certain instances, thereby completely avoiding possibly expensive calls to the inference procedure. We also identify several approximation schemes which can provide further speedup. We instantiate these ideas to the structured prediction task of semantic role labeling and show that we can achieve a speedup of over 2.5 using our approach while retaining the guarantees of exactness and a further speedup of over 3 using approximations that do not degrade performance.