Conference article

Real-valued Syntactic Word Vectors (RSV) for Greedy Neural Dependency Parsing

Ali Basirat
Department of Linguistics and Philology, Uppsala University

Joakim Nivre
Department of Linguistics and Philology, Uppsala University

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

Linköping Electronic Conference Proceedings 131:3, p. 21-28

NEALT Proceedings Series 29:3, p. 21-28

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Published: 2017-05-08

ISBN: 978-91-7685-601-7

ISSN: 1650-3686 (print), 1650-3740 (online)


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.


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