Name
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. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.3115/1119176.1119201 | CoNLL |
Keywords | Field | DocType |
diverse classifier,entity system,english data,f-measure error,combined system,different condition,entity recognition,english development data,classifier-combination experimental framework,classifier combination,additional training resource,markov model,measurement error | Pattern recognition,Computer science,Named entity,Artificial intelligence,Natural language processing,Principle of maximum entropy,Linear classifier,Classifier (linguistics),Hidden Markov model,Named-entity recognition,Machine learning | Conference |
Citations | PageRank | References |
158 | 12.31 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Radu Florian | 1 | 924 | 91.44 |
| Abe Ittycheriah | 2 | 318 | 22.92 |
| Hongyan Jing | 3 | 1524 | 112.18 |
| Zhang, Tong | 4 | 7126 | 611.43 |