Name
Title | ||
|---|---|---|
A probabilistic feature based maximum entropy model for chinese named entity recognition |
Abstract | ||
|---|---|---|
This paper proposes a probabilistic feature based Maximum Entropy (ME) model for Chinese named entity recognition. Where, probabilistic feature functions are used instead of binary feature functions, it is one of the several differences between this model and the most of the previous ME based model. We also explore several new features in our model, which includes confidence functions, position of features etc. Like those in some previous works, we use sub-models to model Chinese Person Names, Foreign Names, location name and organization name respectively, but we bring some new techniques in these sub-models. Experimental results show our ME model combining above new elements brings significant improvements. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11940098_20 | ICCPOL |
Keywords | Field | DocType |
maximum entropy model,previous me,chinese person names,new element,binary feature function,entity recognition,me model,foreign names,probabilistic feature,new feature,probabilistic feature function,new technique,maximum entropy,evaluation | Organization Name,Pattern recognition,Computer science,Natural language,Natural language processing,Artificial intelligence,Principle of maximum entropy,Probabilistic logic,Feature based,Named-entity recognition,Binary number | Conference |
Volume | ISSN | ISBN |
4285 | 0302-9743 | 3-540-49667-X |
Citations | PageRank | References |
2 | 0.48 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suxiang Zhang | 1 | 15 | 6.36 |
| Xiaojie Wang | 2 | 395 | 66.31 |
| Juan Wen | 3 | 11 | 2.68 |
| ying qin | 4 | 8 | 1.11 |
| Yixin Zhong | 5 | 29 | 9.17 |