Semi-joint labeling for chinese named entity recognition

Authors:
Chia-Wei Wu;Richard Tzong-Han Tsai;Wen-Lian Hsu
Affiliations:
Institute of Information Science, Academia Sinica, Taipei, Taiwan;Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan;Institute of Information Science, Academia Sinica, Taipei, Taiwan and Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan
Venue:
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Year:
2008

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Abstract

Named entity recognition (NER) is an essential component of text mining applications. In Chinese sentences, words do not have delimiters; thus, incorporating word segmentation information into an NER model can improve its performance. Based on the framework of dynamic conditional random fields, we propose a novel labeling format, called semi-joint labeling which partially integrates word segmentation information and named entity tags for NER. The model enhances the interaction of segmentation tags and NER achieved by traditional approaches. Moreover, it allows us to consider interactions between multiple chains in a linear-chain model. We use data from the SIGHAN 2006 NER bakeoff to evaluate the proposed model. The experimental results demonstrate that our approach outperforms state-of-the-art systems.