Target word selection is one of the most important and difficult tasks in English-Korean Machine Translation. It effects on the overall translation accuracy of machine translation systems. In this paper, we present a new approach to Korean target word selection for an English noun with translation ambiguities using multiple knowledge such as verb frame patterns, sense vectors based on collocations, statistical Korean local context information and co-occurring POS information. Verb frame patterns constructed with dictionary and corpus play an important role in resolving the sparseness problem of collocation data. Sense vectors are a set of collocation data when an English word having target selection ambiguities is to be translated to specific Korean target word. Statistical Korean Local Context Information is an N-gram information generated using Korean corpus. The co-occurring POS information is a statistically significant POS clue which appears with ambiguous word. To evaluate our approach, we applied the method to Tellus-EK system, English-Korean automatic translation system currently developed at ETRI [1],[2]. The experiment showed promising results for diverse sentences from web documents.
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Ki-Young LEE, Sang-Kyu PARK, Han-Woo KIM, "A Method for English-Korean Target Word Selection Using Multiple Knowledge Sources" in IEICE TRANSACTIONS on Fundamentals,
vol. E89-A, no. 6, pp. 1622-1629, June 2006, doi: 10.1093/ietfec/e89-a.6.1622.
Abstract: Target word selection is one of the most important and difficult tasks in English-Korean Machine Translation. It effects on the overall translation accuracy of machine translation systems. In this paper, we present a new approach to Korean target word selection for an English noun with translation ambiguities using multiple knowledge such as verb frame patterns, sense vectors based on collocations, statistical Korean local context information and co-occurring POS information. Verb frame patterns constructed with dictionary and corpus play an important role in resolving the sparseness problem of collocation data. Sense vectors are a set of collocation data when an English word having target selection ambiguities is to be translated to specific Korean target word. Statistical Korean Local Context Information is an N-gram information generated using Korean corpus. The co-occurring POS information is a statistically significant POS clue which appears with ambiguous word. To evaluate our approach, we applied the method to Tellus-EK system, English-Korean automatic translation system currently developed at ETRI [1],[2]. The experiment showed promising results for diverse sentences from web documents.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.6.1622/_p
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@ARTICLE{e89-a_6_1622,
author={Ki-Young LEE, Sang-Kyu PARK, Han-Woo KIM, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Method for English-Korean Target Word Selection Using Multiple Knowledge Sources},
year={2006},
volume={E89-A},
number={6},
pages={1622-1629},
abstract={Target word selection is one of the most important and difficult tasks in English-Korean Machine Translation. It effects on the overall translation accuracy of machine translation systems. In this paper, we present a new approach to Korean target word selection for an English noun with translation ambiguities using multiple knowledge such as verb frame patterns, sense vectors based on collocations, statistical Korean local context information and co-occurring POS information. Verb frame patterns constructed with dictionary and corpus play an important role in resolving the sparseness problem of collocation data. Sense vectors are a set of collocation data when an English word having target selection ambiguities is to be translated to specific Korean target word. Statistical Korean Local Context Information is an N-gram information generated using Korean corpus. The co-occurring POS information is a statistically significant POS clue which appears with ambiguous word. To evaluate our approach, we applied the method to Tellus-EK system, English-Korean automatic translation system currently developed at ETRI [1],[2]. The experiment showed promising results for diverse sentences from web documents.},
keywords={},
doi={10.1093/ietfec/e89-a.6.1622},
ISSN={1745-1337},
month={June},}
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TY - JOUR
TI - A Method for English-Korean Target Word Selection Using Multiple Knowledge Sources
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1622
EP - 1629
AU - Ki-Young LEE
AU - Sang-Kyu PARK
AU - Han-Woo KIM
PY - 2006
DO - 10.1093/ietfec/e89-a.6.1622
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2006
AB - Target word selection is one of the most important and difficult tasks in English-Korean Machine Translation. It effects on the overall translation accuracy of machine translation systems. In this paper, we present a new approach to Korean target word selection for an English noun with translation ambiguities using multiple knowledge such as verb frame patterns, sense vectors based on collocations, statistical Korean local context information and co-occurring POS information. Verb frame patterns constructed with dictionary and corpus play an important role in resolving the sparseness problem of collocation data. Sense vectors are a set of collocation data when an English word having target selection ambiguities is to be translated to specific Korean target word. Statistical Korean Local Context Information is an N-gram information generated using Korean corpus. The co-occurring POS information is a statistically significant POS clue which appears with ambiguous word. To evaluate our approach, we applied the method to Tellus-EK system, English-Korean automatic translation system currently developed at ETRI [1],[2]. The experiment showed promising results for diverse sentences from web documents.
ER -