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2015

A Neural Probabilistic Structured-Prediction Model for Transition-Based Dependency Parsing

3 years 10 months ago
A Neural Probabilistic Structured-Prediction Model for Transition-Based Dependency Parsing
Neural probabilistic parsers are attractive for their capability of automatic feature combination and small data sizes. A transition-based greedy neural parser has given better accuracies over its linear counterpart. We propose a neural probabilistic structured-prediction model for transition-based dependency parsing, which integrates search and learning. Beam search is used for decoding, and contrastive learning is performed for maximizing the sentence-level log-likelihood. In standard Penn Treebank experiments, the structured neural parser achieves a
Hao Zhou, Yue Zhang, Shujian Huang, Jiajun Chen
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACL
Authors Hao Zhou, Yue Zhang, Shujian Huang, Jiajun Chen
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