Article | Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden | Real-valued Syntactic Word Vectors (RSV) for Greedy Neural Dependency Parsing
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Title:
Real-valued Syntactic Word Vectors (RSV) for Greedy Neural Dependency Parsing
Author:
Ali Basirat: Department of Linguistics and Philology, Uppsala University Joakim Nivre: Department of Linguistics and Philology, Uppsala University
Download:
Full text (pdf)
Year:
2017
Conference:
Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden
Issue:
131
Article no.:
003
Pages:
21-28
No. of pages:
8
Publication type:
Abstract and Fulltext
Published:
2017-05-08
ISBN:
978-91-7685-601-7
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Series:
NEALT Proceedings Series
Publisher:
Linköping University Electronic Press, Linköpings universitet


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We show that a set of real-valued word vectors formed by right singular vectors of a transformed co-occurrence matrix are meaningful for determining different types of dependency relations between words. Our experimental results on the task of dependency parsing confirm the superiority of the word vectors to the other sets of word vectors generated by popular methods of word embedding. We also study the effect of using these vectors on the accuracy of dependency parsing in different languages versus using more complex parsing architectures.

Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden

Author:
Ali Basirat, Joakim Nivre
Title:
Real-valued Syntactic Word Vectors (RSV) for Greedy Neural Dependency Parsing
References:

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Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden

Author:
Ali Basirat, Joakim Nivre
Title:
Real-valued Syntactic Word Vectors (RSV) for Greedy Neural Dependency Parsing
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Last updated: 2017-02-21