Abstract
Named Entity Recognition (NER), an important problem of Natural Language Processing, is the basis for other applications, such as Data Mining and Relation Extraction. With a sequence labeling approach, this paper wants to answer which kind of tokens that should be taken as the graininess in NER task, characters or words. Meanwhile, we use not only local context features within a sentence, but also global knowledge features extracting from other occurrences of each word in the whole corpus. The results show that without the global features the person names and the location names have good result based on characters, but the organization names are more suitable based on words. When global features are added, the performance of based on words improved significantly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lafferty, J., McCallum, A., Pereira, F.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proceedings of the 18th International Conference on Machine Learning (2001)
Chieu, H.L., Ng, H.T.: Named Entity Recognition: A Maximum Entropy Approach Using Global Information. In: Proceedings of the Nineteenth International Conference on Computational Linguistics, pp. 190–196 (2002)
Wu, Y., Zhao, J., Xu, B.: Chinese Named Entity Recognition Model Based on Multiple Features. In: Proceedings of HLT/EMNLP, Vancouver, B.C., Canada, October 6-8, pp. 427–434, (2005)
Chieu, H.L., Ng, H.T.: Named Entity Recognition with a Maximum Entropy Approach (2003)
Krupka, G.R., IsoQuest, K.H.: Description of the NerOwl Extractor System as Used for MUC-7. In: Proceedings of the 7th Message Understanding Conference, Virginia, pp. 21–28 (2005)
Thamar: Exploiting Named Entity Taggers in a Second Language. ACL (2007)
Babych, B., Hartley, A.: Improving machine translation quality with automatic named entity recognition. In: Proceedings of the EACL 2003 Workshop on MT and Other Language Technology Tools (2003)
Krishnan, V., Manning, C.D.: An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition. ACL (2006)
Batchelor, C.R., Corbett, P.T.: Semantic enrichment of journal articles using chemical named entity recognition. ACL (2007)
Chinchor, N.: MUC-7 named entity task definition, version 3.5. In: Proceedings of the Seventh Message Understanding Conference (1998)
Sundheim, B.M.: Named entity task definition, version 2.1. In: Proceedings of the Sixth Message Understanding Conference, pp. 319–332 (1995)
Borthwick, A.: A Maximum Entropy Approach to Named Entity Recognition. Ph.D. thesis, Computer Science Department, New York University (1999)
Bikel, D.M., Schwartz, R., Weischedel, R.M.: An algorithm that learns what’s in a name. Machine Learning, 211–231 (1999)
Sundheim, B.M.: Named entity task definition, version 2.1. In: Proceedings of the Sixth Message Understanding Conference (1995)
Mihalcea, R., Moldovan, D.: Document indexing using named entities. Studies in Informatics and Control, vol. 10 (January 2001)
Mann, G.S.: Fine-grained proper noun ontologies for question answering. In: SemaNet 2002: Building and Using Semantic Networks, Taipei, Taiwan (2002)
Darroch, J., Ratcliff, D.: Generalized iterative scaling for log-linear models. Annals of Mathematical Statistics (1972)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Z., Zhu, C., Zhao, T. (2010). Chinese Named Entity Recognition with a Sequence Labeling Approach: Based on Characters, or Based on Words?. In: Huang, DS., Zhang, X., Reyes GarcÃa, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_78
Download citation
DOI: https://doi.org/10.1007/978-3-642-14932-0_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14931-3
Online ISBN: 978-3-642-14932-0
eBook Packages: Computer ScienceComputer Science (R0)