Lexical Selection for Cross-Language Applications: Combining LCS with
WordNet
Lexical Selection for Cross-Language Applications: Combining LCS with
WordNet
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Date
1998-10-15
Authors
Dorr, Bonnie J.
Katsova, Maria
Advisor
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Abstract
This paper describes experiments for testing the power of large-scale
resources for lexical selection in machine translation (MT) and
cross-language information retrieval (CLIR). We adopt the view that
verbs with similar argument structure share certain meaning components,
but that those meaning components are more relevant to argument
realization than to idiosyncratic verb meaning. We verify this by
demonstrating that verbs with similar argument structure as encoded in
Lexical Conceptual Structure (LCS) are rarely synonymous in WordNet.
We then use the results of this work to guide our implementation of
an algorithm for cross-language selection of lexical items, exploiting
the strengths of each resource: LCS for semantic structure and WordNet
for semantic content. We use the Parka Knowledge-Based System to encode
LCS representations and WordNet synonym sets and we implement our
lexical-selection algorithm as Parka-based queries into a knowledge
base containing both information types.
(Also cross-referenced as UMIACS-TR-98-49)
(Also cross-referenced as LAMP-TR-021)