@inproceedings{cfd0dd18717e49f2ab719cfc911a1448,
title = "A re-ranking model for dependency parsing with knowledge graph embeddings",
abstract = "Re-ranking models of parse trees have been focused on re-ordering parse trees with a syntactic view. However, also a semantic view should be considered in re-ranking parse trees, because the fact that a word pair has a dependency implies that the pair has both syntactic and semantic relations. This paper proposes a re-ranking model for dependency parsing based on a combination of syntactic and semantic plausibilities of dependencies. The syntactic probability is used as a syntactic plausibility of a parse tree, and a knowledge graph embedding is adopted to represent its semantic plausibility. The knowledge graph embedding allows the semantic plausibility of parse trees to be expressed effectively with ease. The experiments on the standard Penn Treebank corpus prove that the proposed model improves the base parser regardless of the number of candidate parse trees.",
keywords = "dependency parsing, knowledge graph embedding, re-ranking",
author = "Kim, \{A. Yeong\} and Song, \{Hyun Je\} and Park, \{Seong Bae\} and Lee, \{Sang Jo\}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; International Conference on Asian Language Processing, IALP 2015 ; Conference date: 24-10-2015 Through 25-10-2015",
year = "2016",
month = apr,
day = "12",
doi = "10.1109/IALP.2015.7451560",
language = "English",
series = "Proceedings of 2015 International Conference on Asian Language Processing, IALP 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "177--180",
editor = "Bin Ma and Min Zhang and Yanfeng Lu and Minghui Dong and Wenliang Chen",
booktitle = "Proceedings of 2015 International Conference on Asian Language Processing, IALP 2015",
}