Paper: Concise Integer Linear Programming Formulations for Dependency Parsing

ACL ID P09-1039
Title Concise Integer Linear Programming Formulations for Dependency Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

We formulate the problem of non- projective dependency parsing as a polynomial-sized integer linear pro- gram. Our formulation is able to handle non-local output features in an efficient manner; not only is it compatible with prior knowledge encoded as hard con- straints, it can also learn soft constraints from data. In particular, our model is able to learn correlations among neighboring arcs (siblings and grandparents), word valency, and tendencies toward nearly- projective parses. The model parameters are learned in a max-margin framework by employing a linear programming relaxation. We evaluate the performance of our parser on data in several natural languages, achieving improvements over existing state-of-the-art methods.