2016 | OriginalPaper | Chapter
A Stack LSTM Transition-Based Dependency Parser with Context Enhancement and K-best Decoding
Authors : Fuxiang Wu, Minghui Dong, Zhengchen Zhang, Fugen Zhou
Published in: Chinese Lexical Semantics
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Transition-based parsing is useful for many NLP tasks. For improving the parsing accuracy, this paper proposes the following two enhancements based on a transition-based dependency parser with stack long short-term memory: using the context of a word in a sentence, and applying K-best decoding to expand the searching space. The experimental results show that the unlabeled and labeled attachment accuracies of our parser improve 0.70% and 0.87% over those of the baseline parser for English respectively, and are 0.82% and 0.86% higher than those of the baseline parser for Chinese respectively.