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2015

A Data-Driven, Factorization Parser for CCG Dependency Structures

3 years 10 months ago
A Data-Driven, Factorization Parser for CCG Dependency Structures
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. Our implementation is available at http://www.icst. pku.edu.cn/lcwm/grass.
Yantao Du, Weiwei Sun, Xiaojun Wan
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACL
Authors Yantao Du, Weiwei Sun, Xiaojun Wan
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