Two approaches for incorporating linguistic constraints to improve the usability of Telugu dependency parser
by R. Rajeswara Rao; B. Venkata Seshu Kumari
International Journal of Applied Pattern Recognition (IJAPR), Vol. 3, No. 2, 2016

Abstract: Statistical systems with high accuracy are very useful in real-world applications. If these systems can capture basic linguistic information, then the usefulness of these statistical systems improves a lot. This paper is an attempt at incorporating linguistic constraints in statistical dependency parsing. We consider a simple linguistic constraint that a verb should not have multiple subjects or direct objects as its children in the dependency tree. We first describe the importance of this constraint considering machine translation systems which use dependency parser output, as an example application. We then show how the current state-of-the-art dependency parsers violate this constraint. We describe two methods to handle this constraint. We evaluate our methods on the state-of-the-art dependency Telugu parser. Our results show that we can build a statistical parser which handles linguistic constraints and thus be more useful in real-world applications without compromising accuracy.

Online publication date: Fri, 09-Sep-2016

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