Name
Abstract | ||
|---|---|---|
Semi-supervised learning is frequently used when we have a small labeled training set but a large set of unlabeled samples. In this paper, we combine Hidden Markov Models and Transformation Based Learning in a semi-supervised learning approach. Self-training and Co-training are the two semi-supervised techniques that we apply to our scheme in order to classify Portuguese noun phrases. Our main goal here is to show that we can achieve effective noun phrase extraction using fewer tagged examples by applying a semi-supervised technique. Our models show good improvement with a small labeled corpus and little with a large one. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11751984_21 | PROPOR |
Keywords | Field | DocType |
noun phrase,hidden markov model,semi supervised learning | Noun phrase,Training set,Semi-supervised learning,Computer science,Portuguese,Speech recognition,Unsupervised learning,Natural language processing,Artificial intelligence,Hidden Markov model,Transformation based learning | Conference |
Volume | ISSN | ISBN |
3960 | 0302-9743 | 3-540-34045-9 |
Citations | PageRank | References |
3 | 0.42 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruy Luiz Milidiú | 1 | 192 | 20.18 |
| Cícero Nogueira dos Santos | 2 | 771 | 37.83 |
| Julio C. Duarte | 3 | 26 | 2.46 |
| Raúl P. Renterı́a | 4 | 47 | 5.77 |