Name
Abstract | ||
|---|---|---|
The goal of a classifier is to accurately predict the value of a class given its context. Often the number of classes “competing” for each prediction is large. Therefore, it is necessary to “focus attention” on a smaller subset of these. We investigate the contribution of a “focus of attention” mechanism using enablers to the performance of a word predictor. We then describe a large scale experimental study in which the approach presented is shown to yield significant improvements in word prediction tasks |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/SCCC.2000.890398 | SCCC |
Keywords | Field | DocType |
classification,speech recognition,probability distribution,merging,computer science,text analysis,learning artificial intelligence,artificial intelligence,testing | Computer science,Probability distribution,Natural language processing,Artificial intelligence,Classifier (linguistics),Merge (version control),Machine learning | Conference |
ISSN | ISBN | Citations |
1522-4902 | 0-7695-0810-3 | 0 |
PageRank | References | Authors |
0.34 | 10 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Even-Zohar, Y. | 1 | 0 | 0.34 |