Please use this identifier to cite or link to this item: http://ir.inflibnet.ac.in/handle/1944/501
Title: Performance of Memoized- Most- Likelihood Parsing in Disambiguation Process
Authors: Ingle, Maya
Chandwani, M
Keywords: Natural Language Processing
Disambiguation
Statistical Parsing
Character Recognition
Issue Date: 2-Feb-2005
Publisher: 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.
URI: http://hdl.handle.net/1944/501
ISBN: 81-902079-0-3
Appears in Collections:CALIBER 2005:Kochi

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