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A reestimation algorithm for probabilistic dependency grammars

Published online by Cambridge University Press:  01 September 1999

SEUNGMI LEE  and
KEY-SUN CHOI
SEUNGMI LEE
Affiliation:
Department of Computer Science, Center for Artificial Intelligence Research, Korea Advanced Institute of Science and Technology, 373-1 Kusung-Dong YuSung-Gu Taejon 305-701 Korea; e-mail: leesm@world.kaist.ac.kr, kschoi@world.kaist.ac.kr
KEY-SUN CHOI
Affiliation:
Department of Computer Science, Center for Artificial Intelligence Research, Korea Advanced Institute of Science and Technology, 373-1 Kusung-Dong YuSung-Gu Taejon 305-701 Korea; e-mail: leesm@world.kaist.ac.kr, kschoi@world.kaist.ac.kr
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Abstract

A probabilistic parameter reestimation algorithm plays a key role in the automatic acquisition of stochastic grammars. In the case of context-free phrase structure grammars, the inside-outside algorithm is widely used. However, it is not directly applicable to Probabilistic Dependency Grammar (PDG), because PDG is not based on constituents but on a head-dependent relation between pairs of words. This paper presents a reestimation algorithm which is a variation of the inside-outside algorithm adapted to probabilistic dependency grammar. The algorithm can be used either to reestimate the probabilistic parameters of an existing dependency grammar, or to extract a PDG from scratch. Using the algorithm, we have learned a PDG from a part-of-speech-tagged corpus of Korean, which showed about 62·82% dependency accuracy (the percentage of correct dependencies) for unseen test sentences.

Type
Research Article
Information
Natural Language Engineering , Volume 5 , Issue 3 , September 1999 , pp. 251 - 270
Copyright
© 1999 Cambridge University Press

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