@inproceedings{99fceae4819e4bc89b2fd8a13de5a248,
title = "A stack LSTM transition-based dependency parser with context enhancement and K-best decoding",
abstract = "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.",
keywords = "Context enhancement, K-best decoding, LSTM, NLP, Transition-based dependency parsing",
author = "Fuxiang Wu and Minghui Dong and Zhengchen Zhang and Fugen Zhou",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 17th Chinese Lexical Semantics Workshop, CLSW 2016 ; Conference date: 20-05-2016 Through 22-05-2016",
year = "2016",
doi = "10.1007/978-3-319-49508-8\_37",
language = "英语",
isbn = "9783319495071",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "397--404",
editor = "Jingxia Lin and Xuri Tang and Minghui Dong",
booktitle = "Chinese Lexical Semantics - 17th Workshop, CLSW 2016, Revised Selected Papers",
address = "德国",
}