Lauma Pretkalnina
Institute of Mathematics and Computer Science, University of Latvia, Riga, Latvia
Laura Rituma
Institute of Mathematics and Computer Science, University of Latvia, Riga, Latvia
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Ingår i: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013); May 22-24; 2013; Oslo University; Norway. NEALT Proceedings Series 16
Linköping Electronic Conference Proceedings 85:25, s. 279-289
NEALT Proceedings Series 16:25, p. 279-289
Publicerad: 2013-05-17
ISBN: 978-91-7519-589-6
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
Syntactic parsing is an important technique in the natural language processing; yet Latvian is still lacking an efficient general coverage syntax parser. This paper reports on the first experiments on statistical syntactic parsing for Latvian — a highly inflective Indo-European language with a relatively free word order. We have induced a statistical parser from
small; non-balanced Latvian Treebank using the MaltParser toolkit and measured the unlabeled attachment score (UAS). As MaltParser is based on the dependency grammar approach; we have also developed a convertor from the hybrid dependency-based annotation model used in the Latvian Treebank to the pure dependency annotation model. We have obtained a promising 74.63% UAS in 10-fold cross-validation using only ~2500 sentences. The results revealed that best results can be achieved using non-projective stack parsing algorithm with lazy arc adding strategy; but comparably good results can be achieved using projective parsing algorithms combined with appropriate projectiviziation preprocessing.
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