In this paper, the UNITOR system participating in the SemEval-2014 Aspect Based Sentiment Analysis competition is presented. The task is tackled exploiting Kernel Methods within the Support Vector Machine framework. The Aspect Term Extraction is modeled as a sequential tagging task, tackled through SVMhmm. The Aspect Term Polarity, Aspect Category and Aspect Category Polarity detection are tackled as a classification problem where multiple kernels are linearly combined to generalize several linguistic information. In the challenge, UNITOR system achieves good results, scoring in almost all rankings between the 2nd and the 8th position within about 30 competitors.
Castellucci, G., Filice, S., Croce, D., Basili, R. (2014). UNITOR: Aspect Based Sentiment Analysis with Structured Learning. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (pp.761-767). Association for Computational Linguistics (ACL) [10.3115/v1/S14-2135].
UNITOR: Aspect Based Sentiment Analysis with Structured Learning
Castellucci G.;Filice S.;Croce D.;Basili R.
2014-01-01
Abstract
In this paper, the UNITOR system participating in the SemEval-2014 Aspect Based Sentiment Analysis competition is presented. The task is tackled exploiting Kernel Methods within the Support Vector Machine framework. The Aspect Term Extraction is modeled as a sequential tagging task, tackled through SVMhmm. The Aspect Term Polarity, Aspect Category and Aspect Category Polarity detection are tackled as a classification problem where multiple kernels are linearly combined to generalize several linguistic information. In the challenge, UNITOR system achieves good results, scoring in almost all rankings between the 2nd and the 8th position within about 30 competitors.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
