Name
Abstract | ||
|---|---|---|
The vast amount of electronic documents available on the Internet demands for automatic tools that help people finding, organizing and easily accessing to all this information. Although current text classification methods have alleviated some of the above problems, such strategies depend on having a large and reliable set of labeled data. In order to overcome such limitation, this work proposes an alternative approach for semi-supervised text classification, which is based on a new strategy for diminishing the sensitivity to the noise contained on labeled data by means of automatic text summarization. Experimental results showed that our proposed approach outperforms traditional semi-supervised text classification techniques; additionally, our results also indicate that our approach is suitable for learning from only one labeled example per category. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-47955-2_10 | ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2016 |
Keywords | Field | DocType |
Text classification,Text summarization,Semi-supervised learning,Self-training,Feature selection | Automatic summarization,Semi-supervised learning,Information retrieval,Feature selection,Computer science,Labeled data,Self training,The Internet | Conference |
Volume | ISSN | Citations |
10022 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Esaú Villatoro-Tello | 1 | 14 | 3.06 |
| Emmanuel Anguiano-Hernández | 2 | 2 | 0.71 |
| Manuel Montes-Y-Gómez | 3 | 638 | 83.97 |
| Luis Villaseñor-Pineda | 4 | 403 | 53.74 |
| Gabriela Ramírez-De-La-Rosa | 5 | 10 | 10.81 |