A data-driven, factorization parser for CCG dependency structures

Abstract

This paper is concerned with building CCG-grounded, semantics-oriented deep dependency structures with a data-driven, factorization model. Three types of factorization together with different higherorder features are designed to capture different syntacto-semantic properties of functor-Argument dependencies. Integrating heterogeneous factorizations results in intractability in decoding. We propose a principled method to obtain optimal graphs based on dual decomposition. Our parser obtains an unlabeled f-score of 93.23 on the CCGBank data, resulting in an error reduction of 6.5% over the best published result. which yields a significant improvement over the best published result in the literature.