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Best parse parsing with Earley's and Inside algorithms on probabilistic RTN

Published online by Cambridge University Press:  12 September 2008

Young S. Han  and
Key-Sun Choi
Young S. Han
Affiliation:
Korea Advanced Institute of Science and TechnologyTaejon, Korea
Key-Sun Choi
Affiliation:
Korea Advanced Institute of Science and TechnologyTaejon, Korea
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Abstract

Inside parsing is a best parse parsing method based on the Inside algorithm that is often used in estimating probabilistic parameters of stochastic context free grammars. It gives a best parse in O(N3G3) time where N is the input size and G is the grammar size. Earley algorithm can be made to return best parses with the same complexity in N.

By way of experiments, we show that Inside parsing can be more efficient than Earley parsing with sufficiently large grammar and sufficiently short input sentences. For instance, Inside parsing is better with sentences of 16 or less words for a grammar containing 429 states. In practice, parsing can be made efficient by employing the two methods selectively.

The redundancy of Inside algorithm can be reduced by the topdown filtering using the chart produced by Earley algorithm, which is useful in training the probabilistic parameters of a grammar. Extensive experiments on Penn Tree corpus show that the efficiency of Inside computation can be improved by up to 55%.

Type
Articles
Information
Natural Language Engineering , Volume 1 , Issue 2 , June 1995 , pp. 147 - 161
Copyright
Copyright © Cambridge University Press 1995

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