Towards an N-Version Dependency Parser

Maltparser is a contemporary dependency parsing machine learning-based system that shows great accuracy. However 90% for Labelled Attachment Score (LAS) seems to be a de facto limit for such kinds of parsers. Since generally such systems can not be modified, previous works have been developed to study what can be done with the training corpora in order to improve parsing accuracy. High level techniques, such as controlling sentences' length or corpora's size, seem useless for these purposes. But low level techniques, based on an in-depth study of the errors produced by the parser at the word level, seem promising. Prospective low level studies suggested the development of n-version parsers. Each one of these n versions should be able to tackle a specific kind of dependency parsing at the word level and the combined action of all them should reach more accurate parsings. In this paper we present an extensive study on the usefulness and the expected limits for n-version parser to improve parsing accuracy. This work has been developed specifically for Spanish using Maltparser.