Year: 2009
Type: conference-paper
Abstract: We formulate the problem of non-projective dependency parsing as a polynomial-sized integer linear program. 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 constraints, 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.
Institutions: Instituto Superior Técnico, Carnegie Mellon University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: III-COR+RI: Novel Statistical Models and Algorithms for Network Modeling, Mining and Reverse Engineering, Indexing, Mining and Modeling Spatio-Temporal Patterns of Gene Expressions, CAREER: Novel Statistical Models and Computational Algorithms for Evolutionary Genomics, G7042103515, SGER: Scaling up unsupervised grammar induction
OpenAlex ID: W2142603219