Thumbnail
Access Restriction
Open

Author Ingle, Maya ♦ Chandwani, M.
Source Inflibnet's Institutional Repository
Content type Text
Publisher INFLIBNET Centre
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science ♦ Library & information sciences
Subject Keyword Natural Language Processing ♦ Disambiguation ♦ Statistical Parsing ♦ Character Recognition
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.
ISBN 8190207903
Education Level UG and PG
Learning Resource Type Article