Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
Integer linear programming inference for conditional random fields
ICML '05 Proceedings of the 22nd international conference on Machine learning
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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
Computational Linguistics
Importance of linguistic constraints in statistical dependency parsing
ACLstudent '10 Proceedings of the ACL 2010 Student Research Workshop
Analyzing and integrating dependency parsers
Computational Linguistics
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Our approach to dependency parsing is based on the linear model of McDonald et al.(McDonald et al., 2005b). Instead of solving the linear model using the Maximum Spanning Tree algorithm we propose an incremental Integer Linear Programming formulation of the problem that allows us to enforce linguistic constraints. Our results show only marginal improvements over the non-constrained parser. In addition to the fact that many parses did not violate any constraints in the first place this can be attributed to three reasons: 1) the next best solution that fulfils the constraints yields equal or less accuracy, 2) noisy POS tags and 3) occasionally our inference algorithm was too slow and decoding timed out.