Performance of Memoized- Most- Likelihood Parsing in Disambiguation Process
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Date
2005-02-02
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INFLIBNET Centre
Abstract
In this paper, we first present a memoized parsing method for reducing the efforts of computation
in parsing the strings/ sentences of a formal’ natural language. We then discuss the statistical
parsing that extracts the maximum/ most likelihood parse amongst the several parses of a
string/ sentence in formal and natural domain as the most appropriate representative in
disambiguation process. We integrate the statistical and memoized parsing together to
achieve an efficient parsing technique. This integrated approach allows us to obtain the
memoized-most-likelihood parse. Memoized-most-likelihood parse has an additional
performance strength in the sense that it is highly useful further in parsing semantics.
Description
Keywords
Natural Language Processing, Disambiguation, Statistical Parsing, Character Recognition