Efficient Algorithms for Parsing the DOP Model (1996)
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BibTeX
@MISC{Goodman96efficientalgorithms,
author = {Joshua Goodman},
title = {Efficient Algorithms for Parsing the DOP Model},
year = {1996}
}
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Abstract
Excellent results have been reported for DataOriented Parsing (DOP) of natural language texts (Bod, 1993c). Unfortunately, existing algorithms are both computationally intensive and difficult to implement. Previous algorithms are expensive due to two factors: the exponential number of rules that must be generated and the use of a Monte Carlo p arsing algorithm. In this paper we solve the first problem by a novel reduction of the DOP model toga small, equivalent probabilistic context-free grammar. We solve the second problem by a novel deterministic parsing strategy that maximizes the expected number of correct con- stituents, rather than the probability of a correct parse tree. Using ithe optimizations, experiments yield a 97% crossing brackets rate and 88% zero crossing brackets rate. This differs significantly from the results reported by Bod, and is compara- ble to results from a duplication of Pereira and Schabes's (1992) experiment on the same data. We show that Bod's results are at least partially due to an extremely fortuitous choice of test data, and partially due to using cleaner data than other researchers.
