Abstract
When building a Chinese named entity recognition system, one must deal with certain language-specific issues such as whether the model should be based on characters or words. While there is no unique answer to this question, we discuss in detail advantages and disadvantages of each model, identify problems in segmentation and suggest possible solutions, presenting our observations, analysis, and experimental results. The second topic of this paper is classifier combination. We present and describe four classifiers for Chinese named entity recognition and describe various methods for combining their outputs. The results demonstrate that classifier combination is an effective technique of improving system performance: experiments over a large annotated corpus of fine-grained entity types exhibit a 10% relative reduction in F-measure error.
Original language | English (US) |
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Pages | 200-207 |
Number of pages | 8 |
DOIs | |
State | Published - 2003 |
Externally published | Yes |
Event | 8th Conference on Empirical Methods in Natural Language Processing, EMNLP 2003 - Sapporo, Japan Duration: Jul 11 2003 → Jul 12 2003 |
Conference
Conference | 8th Conference on Empirical Methods in Natural Language Processing, EMNLP 2003 |
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Country/Territory | Japan |
City | Sapporo |
Period | 7/11/03 → 7/12/03 |
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Information Systems