TY - GEN
T1 - A robust named-entity recognition system using syllable Bigram embedding with Eojeol prefix information
AU - Kwon, Sunjae
AU - Ko, Youngjoong
AU - Seo, Jungyun
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - Korean named-entity recognition (NER) systems have been developed mainly on the morphological-level, and they are commonly based on a pipeline framework that identifies named-entities (NEs) following the morphological analysis. However, this framework can mean that the performance of NER systems is degraded, because errors from the morphological analysis propagate into NER systems. This paper proposes a novel syllable-level NER system, which does not require a morphological analysis and can achieve a similar or better performance compared with the morphological-level NER systems. In addition, because the proposed system does not require a morphological analysis step, its processing speed is about 1.9 times faster than those of the previous morphological-level NER systems.
AB - Korean named-entity recognition (NER) systems have been developed mainly on the morphological-level, and they are commonly based on a pipeline framework that identifies named-entities (NEs) following the morphological analysis. However, this framework can mean that the performance of NER systems is degraded, because errors from the morphological analysis propagate into NER systems. This paper proposes a novel syllable-level NER system, which does not require a morphological analysis and can achieve a similar or better performance compared with the morphological-level NER systems. In addition, because the proposed system does not require a morphological analysis step, its processing speed is about 1.9 times faster than those of the previous morphological-level NER systems.
KW - Eojeol prefix information
KW - Korean syllable-level named-entity recognition
KW - Syllable bigram embedding
UR - https://www.scopus.com/pages/publications/85037344322
U2 - 10.1145/3132847.3133105
DO - 10.1145/3132847.3133105
M3 - Conference contribution
AN - SCOPUS:85037344322
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2139
EP - 2142
BT - CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Y2 - 6 November 2017 through 10 November 2017
ER -