Conference article

Replacing OOV Words For Dependency Parsing With Distributional Semantics

Prasanth Kolachina
Department of Computer Science and Engineering, University of Gothenburg, Sweden

Martin Riedl
Language Technology Group, Universit¨at Hamburg, Germany

Chris Biemann
Language Technology Group, Universit¨at Hamburg, Germany

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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 131:2, p. 11-20

NEALT Proceedings Series 29:2, p. 11-20

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Published: 2017-05-08

ISBN: 978-91-7685-601-7

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

Abstract

Lexical information is an important feature in syntactic processing like part-ofspeech (POS) tagging and dependency parsing. However, there is no such information available for out-of-vocabulary (OOV) words, which causes many classification errors. We propose to replace OOV words with in-vocabulary words that are semantically similar according to distributional similar words computed from a large background corpus, as well as morphologically similar according to common suffixes. We show performance differences both for count-based and dense neural vector-based semantic models. Further, we discuss the interplay of POS and lexical information for dependency parsing and provide a detailed analysis and a discussion of results: while we observe significant improvements for count-based methods, neural vectors do not increase the overall accuracy.

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