Abstract
This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classifiers (robust linear classifier, maximum entropy, transformation-based learning, and hidden Markov model) are combined under different conditions. When no gazetteer or other additional training resources are used, the combined system attains a performance of 91.6F on the English development data; integrating name, location and person gazetteers, and named entity systems trained on additional, more general, data reduces the F-measure error by a factor of 15 to 21% on the English data.
| Original language | English (US) |
|---|---|
| Pages | 168-171 |
| Number of pages | 4 |
| State | Published - 2003 |
| Externally published | Yes |
| Event | 7th Conference on Natural Language Learning, CoNLL 2003 at HLT-NAACL 2003 - Edmonton, Canada Duration: May 31 2003 → Jun 1 2003 |
Conference
| Conference | 7th Conference on Natural Language Learning, CoNLL 2003 at HLT-NAACL 2003 |
|---|---|
| Country/Territory | Canada |
| City | Edmonton |
| Period | 5/31/03 → 6/1/03 |
ASJC Scopus subject areas
- Management Science and Operations Research
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Modeling and Simulation