The use of abstracted knowledge from an automatically sense-tagged corpus for lexical transfer ambiguity resolution

Hui-Feng Li, Namwon Heo. Kyounghi Moon, Jong-Hyeok Lee


Abstract
This paper proposes a method for lexical transfer ambiguity resolution using corpus and conceptual information. Previous researches have restricted the use of linguistic knowledge to the lexical level. Since the extracted knowledge is stored in words themselves, these methods require a large amount of space with a low recall rate. On the contrary, we resolve word sense ambiguity by using concept co-occurrence information extracted from an automatically sense-tagged corpus. In one experiment, it achieved, on average, a precision of 82.4% for nominal words, and 83% for verbal words. Considering that the test corpus is completely irrelevant to the learning corpus, this is a promising result.
Anthology ID:
1999.mtsummit-1.57
Volume:
Proceedings of Machine Translation Summit VII
Month:
September 13-17
Year:
1999
Address:
Singapore, Singapore
Venue:
MTSummit
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Pages:
390–396
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URL:
https://aclanthology.org/1999.mtsummit-1.57
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Cite (ACL):
Hui-Feng Li, Namwon Heo. Kyounghi Moon, and Jong-Hyeok Lee. 1999. The use of abstracted knowledge from an automatically sense-tagged corpus for lexical transfer ambiguity resolution. In Proceedings of Machine Translation Summit VII, pages 390–396, Singapore, Singapore.
Cite (Informal):
The use of abstracted knowledge from an automatically sense-tagged corpus for lexical transfer ambiguity resolution (Li et al., MTSummit 1999)
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https://preview.aclanthology.org/update-css-js/1999.mtsummit-1.57.pdf