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Dinesh Tummala

dblp:65/4203 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1997
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Information extraction and text analysis · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis
ambiguity resolution
0.011997
Ambiguity Resolution for Machine Translation of Telegraphic Messages · ACL 1997
Natural language and speech › Information extraction and text analysis
syntactic parsing
0.011997
Ambiguity Resolution for Machine Translation of Telegraphic Messages · ACL 1997

Methods — techniques the papers use, named apart from their topics

syntactic grammar · 0.0lexicalized grammar · 0.0grammar design · 0.0
YearPublicationVenuePosition
1997 Ambiguity Resolution for Machine Translation of Telegraphic Messages
abstract
Telegraphic messages with numerous instances of omission pose a new challenge to parsing in that a sentence with omission causes a higher degree of ambiguity than a sentence without omission. Misparsing induced by omissions has a far-reaching consequence in machine translation. Namely, a misparse of the input often leads to a translation into the target language which has incoherent meaning in the given context. This is more frequently the case if the structures of the source and target languages are quite different, as in English and Korean. Thus, the question of how we parse telegraphic messages accurately and efficiently becomes a critical issue in machine translation. In this paper we describe a technical solution for the issue, and present the performance evaluation of a machine translation system on telegraphic messages before and after adopting the proposed solution. The solution lies in a grammar design in which lexicalized grammar rules defined in terms of semantic categories and syntactic rules defined in terms of part-of-speech are utilized together. The proposed grammar achieves a higher parsing coverage without increasing the amount of ambiguity/misparsing when compared with a purely lexicalized semantic grammar, and achieves a lower degree of ambiguity/misparses without decreasing the parsing coverage when compared with a purely syntactic grammar.
Young-Suk Lee 0001, Clifford J. Weinstein, Stephanie Seneff, Dinesh Tummala
ACL4
1996 Automatic English-to-Korean Text Translation of Telegraphic Messages in a Limited Domain
Clifford J. Weinstein, Dinesh Tummala, Young-Suk Lee 0001, Stephanie Seneff
COLING2