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سند علمی

UNITOR: Aspect Based Sentiment Analysis with Structured Learning

Giuseppe Castellucci, Simone Filice, Danilo Croce, Roberto Basili

چکیده

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.

اسناد مشابه

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