Performance of Memoized- Most- Likelihood Parsing in Disambiguation Process
| dc.contributor.author | Ingle, Maya | en_US |
| dc.contributor.author | Chandwani, M | en_US |
| dc.date.accessioned | 2005-05-10T09:50:32Z | en_US |
| dc.date.accessioned | 2010-04-08T08:47:54Z | |
| dc.date.available | 2005-05-10T09:50:32Z | en_US |
| dc.date.available | 2010-04-08T08:47:54Z | |
| dc.date.issued | 2005-02-02 | en_US |
| dc.description.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. | en_US |
| dc.format.extent | 219917 bytes | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.isbn | 81-902079-0-3 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1944/501 | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | INFLIBNET Centre | en_US |
| dc.subject | Natural Language Processing | en_US |
| dc.subject | Disambiguation | en_US |
| dc.subject | Statistical Parsing | en_US |
| dc.subject | Character Recognition | en_US |
| dc.title | Performance of Memoized- Most- Likelihood Parsing in Disambiguation Process | en_US |
| dc.type | Article | en_US |