Name
Abstract | ||
|---|---|---|
In this paper, a new topic identification method, WSIM, is investigated. It exploits the similarity between words and topics. This measure is a function of the similarity between words, based on the mutual information. The performance of WSIM is compared to the cache model and to the well- known SVM classifier. Their behavior is also studied in terms of recall and precision, according to the training siz e. Performance of WSIM reaches % correct topic identi- fication. It outperforms SVM ( %) and has a comparable performance with the cache model ( %). |
Year | Venue | Keywords |
|---|---|---|
2002 | INTERSPEECH | mutual information |
Field | DocType | Citations |
Pattern recognition,Cache,Computer science,Support vector machine,Precision and recall,Speech recognition,Mutual information,Artificial intelligence,Svm classifier,Language model | Conference | 3 |
PageRank | References | Authors |
0.45 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Armelle Brun | 1 | 138 | 21.49 |
| Kamel Smaïli | 2 | 120 | 25.18 |
| Jean-Paul Haton | 3 | 380 | 65.42 |