Statistical syntactic parsing for Latvian

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 a 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.