Improving Data Driven Dependency Parsing using Clausal Information

Abstract

The paper describes a data driven dependency parsing approach which uses information about the clauses in a sentence to improve the parser performance. The clausal information is added automatically using a partial parser. We demonstrate the experiments on Hindi, a morphologically rich, free-word-order lan-guage, using a modified version of MSTPars-er. We did all the experiments on the ICON 2009 parsing contest data. We achieved an improvement of 0.87% and 0.77% in unla-beled attachment and labeled attachment accu-racies respectively over the baseline parsing accuracies.