@inproceedings{aa7cdd9d742e40179de0691be160181f,
title = "SLDP: Sequence learning dependency parsing model using long short-term memory",
abstract = "Recent work on neural network models shows success in dependency parsing. In this paper, we present a sequence learning dependency parsing (SLDP) model using long short-term memory for shift-reduce parser. A feed-forward neural network is used to build greedy model from rich local features. With the features extracted by the local model, we further train a long short-term memory (LSTM) model optimized for global parsing sequences. Our model has the capability of learning not only atomic feature combinations automatically but also the long distance dependent information for dependency parsing. Experiments on English Penn Treebank show that our SLDP model significantly outperforms the baseline, achieving 90.7\% unlabeled attachment score and 89.0\% labeled attachment score.",
keywords = "Dependency parsing, Long short-term memory, Natural language processing, Neural networks, Syntactic parsing",
author = "Zhou, \{Qing Yu\} and Zheng, \{De Quan\} and Zhao, \{Tie Jun\}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016 ; Conference date: 10-07-2016 Through 13-07-2016",
year = "2016",
month = jul,
day = "2",
doi = "10.1109/ICMLC.2016.7860886",
language = "英语",
series = "Proceedings - International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Computer Society",
pages = "111--116",
booktitle = "Proceedings of 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016",
address = "美国",
}