Name
Abstract | ||
|---|---|---|
Recent literature on text-tagging reported successful results by applying Maximum Entropy (ME) models. In general, ME taggers rely on carefully selected binary features, which try to capture discrimi- nant information from the training data. This paper introduces a standard setting of binary features, inspired by the litera- ture on named-entity recognition and text chunking, and derives corresponding real- valued features based on smoothed log- probabilities. The resulting ME models have orders of magnitude fewer parame- ters. Effective use of training data to esti- mate features and parameters is achieved by integrating a leaving-one-out method into the standard ME training algorithm. Experimental results on two tagging tasks show statistically significant performance gains after augmenting standard binary- feature models with real-valued features. |
Year | Venue | DocType |
|---|---|---|
2006 | Learning Structured Information@EACL | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vanessa Sandrini | 1 | 8 | 2.00 |
| marcello federico | 2 | 2420 | 179.56 |
| mauro cettolo | 3 | 539 | 55.91 |