Cross-domain Polarity Classification using a Knowledge-enhanced Meta-classifier

Autores UPV
Revista Knowledge-Based Systems


In this paper, we propose the use of meta-learning to combine and enrich those approaches by adding also other knowledge-based features. In addition to the aforementioned classical approaches, our system uses the BabelNet multilingual semantic network to generate features derived from word sense disambiguation and vocabulary expansion. Experimental results show state-of-theart performance on single and cross-domain polarity classification.