A Robust Named-Entity Recognition System Using Syllable Bigram Embedding with Eojeol Prefix Information

被引：3

作者：

Kwon, Sunjae ^{[1
]}

Ko, Youngjoong ^{[2
]}

Seo, Jungyun ^{[3
]}

机构：

[1] Ulsan Natl Inst Sci & Technol, Sch Elec & Comp Engn, Ulsan 44919, South Korea

[2] Dong A Univ, Dept Comp Engn, Busan 49315, South Korea

[3] Sogang Univ, Dept Comp Engn, Seoul 04107, South Korea

来源：

CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT | 2017年

基金：

新加坡国家研究基金会;

关键词：

Korean syllable-level named-entity recognition; Syllable bigram embedding; Eojeol prefix information;

D O I：

10.1145/3132847.3133105

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：2139 / 2142

页数：4

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