Improving Parsing Accuracy for Spanish using Maltparser

Miguel Ballesteros , Jesús Herrera , Virginia Francisco , Pablo Gervás


In the last years, dependency parsing has been accomplished by machine learning-based systems showing great accuracy but usually under 90% for Labelled Attachment Score (LAS). Maltparser is one of such systems. Machine learning allows to obtain parsers for every language having an adequate training corpus. Since generally such systems can not be modified the following question arises: Can we beat this 90% LAS by using better training corpora? In the present paper we show some prospective works on it. We studied some strategies considering training corpus' size and its sentences' length in order to obtain better parsing accuracy.

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