Abstract
Name tagging is a critical early stage in many natural language processing pipelines. In this paper we analyze the types of errors produced by a tagger, distinguishing name classification and various types of name identification errors. We present a joint inference model to improve Chinese name tagging by incorporating feedback from subsequent stages in an information extraction pipeline: name structure parsing, cross-document coreference, semantic relation extraction and event extraction. We show through examples and performance measurement how different stages can correct different types of errors. The resulting accuracy approaches that of individual human annotators.
| Original language | English (US) |
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| Pages | 420-427 |
| Number of pages | 8 |
| State | Published - 2006 |
| Event | 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 - Sydney, NSW, Australia Duration: Jul 17 2006 → Jul 21 2006 |
Other
| Other | 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 |
|---|---|
| Country/Territory | Australia |
| City | Sydney, NSW |
| Period | 7/17/06 → 7/21/06 |
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Modeling and Simulation
- Human-Computer Interaction