Name
Abstract | ||
|---|---|---|
The Internet has seen a surge of new types of repositories with free access and collaborative open edition. However, this large amount of information, made available democratically and virtually without any control, raises questions about its quality. In this work, we investigate the use of meta-learning techniques to combine sets of semantically related quality indicators (aka, views) in order to automatically assess the quality of wiki articles. The idea is inspired on the combination of multiple (quality) experts. We perform a thorough analysis of the proposed multiview-based meta-learning approach in 3 collections. In our experiments, meta-learning was able to improve the performance of a state-of-the-art method in all tested datasets, with gains of up to 27% in quality assessment. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33290-6_26 | TPDL |
Keywords | Field | DocType |
quality assessment,collaborative open edition,new type,wiki article,proposed multiview-based meta-learning approach,large amount,automatic quality assessment,semantically related quality indicator,thorough analysis,state-of-the-art method,free access,meta-learning technique | Data mining,Information retrieval,Computer science,Support vector machine,Mean squared error,AKA,The Internet | Conference |
Citations | PageRank | References |
3 | 0.41 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Hasan Dalip | 1 | 140 | 11.56 |
| Marcos André Gonçalves | 2 | 2740 | 191.03 |
| Marco Cristo | 3 | 618 | 39.30 |
| Pável Calado | 4 | 809 | 55.33 |