Name
Abstract | ||
|---|---|---|
Real world applications of machine learning in natural language processing can span many different domains and usually require a huge effort for the annotation of domain specific training data. For this reason, domain adaptation techniques have gained a lot of attention in the last years. In order to derive an effective domain adaptation, a good feature representation across domains is crucial as well as the generalisation ability of the predictive model. In this paper we address the problem of domain adaptation for sentiment classification by combining deep learning, for acquiring a cross-domain high-level feature representation, and ensemble methods, for reducing the cross-domain generalization error. The proposed adaptation framework has been evaluated on a benchmark dataset composed of reviews of four different Amazon category of products, significantly outperforming the state of the art methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICTAI.2016.0037 | 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) |
Keywords | Field | DocType |
Domain Adaptation,Transfer Learning,Ensemble,Deep Learning,Sentiment Analysis | Noise reduction,Annotation,Pattern recognition,Domain adaptation,Generalization,Computer science,Support vector machine,Effective domain,Artificial intelligence,Deep learning,Ensemble learning,Machine learning | Conference |
ISSN | ISBN | Citations |
1082-3409 | 978-1-5090-4460-3 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Debora Nozza | 1 | 6 | 3.81 |
| Elisabetta Fersini | 2 | 140 | 20.70 |
| Enza Messina | 3 | 214 | 23.18 |