Name
Abstract | ||
|---|---|---|
We have participated in three open tracks of Chinese word segmentation and named entity recognition tasks of SIGHAN Bakeoff3. We take a probabilistic feature based Maximum Entropy (ME) model as our basic frame to combine multiple sources of knowledge. Our named entity recognizer achieved the highest F measure for MSRA, and word segmenter achieved the medium F measure for MSRA. We find effective combining of the external multi-knowledge is crucial to improve performance of word segmentation and named entity recognition. |
Year | Venue | DocType |
|---|---|---|
2006 | SIGHAN@COLING/ACL | Conference |
Citations | PageRank | References |
6 | 0.63 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suxiang Zhang | 1 | 15 | 6.36 |
| ying qin | 2 | 8 | 1.11 |
| juan wen | 3 | 6 | 0.63 |
| Xiaojie Wang | 4 | 395 | 66.31 |