Two Approaches for Building an Unsupervised Dependency Parser and their Other Applications

Jagadeesh Gorla, Amit Goyal, Rajeev Sangal

Much work has been done on building a parser for natural languages, but most of this work has concentrated on supervised parsing. Unsupervised parsing is a less explored area, and unsupervised dependency parser has hardly been tried. In this paper we present two approaches for building an unsupervised dependency parser. One approach is based on learning dependency relations and the other on learning subtrees. We also propose some other applications of these approaches.

Subjects: 13. Natural Language Processing; 12. Machine Learning and Discovery

Submitted: Apr 9, 2007

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