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

Sunjae Kwon, Youngjoong Ko, Jungyun Seo
2017 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17  
Korean named-entity recognition (NER) systems have been developed mainly on the morphological-level, and they are commonly based on a pipeline framework that identi es 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. is paper proposes a novel syllable-level NER system, which does not require a morphological analysis and can
more » ... ysis and can achieve a similar or be er 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.
doi:10.1145/3132847.3133105 dblp:conf/cikm/KwonKS17 fatcat:s6gkh75yfrbclfwlv7lfd6qsj4