AbstractIn this paper, we study the problems of opinion expression extraction and expression-level polarity and intensity classification. Traditional fine-grained opinion analysis systems address these problems in isolation and thus cannot capture interactions among the textual spans of opinion expressions and their opinion-related properties. We present two types of joint approaches that can account for such interactions during 1) both learning and inference or 2) only during inference. Extensive experiments on a standard dataset demonstrate that our approaches provide substantial improvements over previously published results. By analyzing the results, we gain some insight into the advantages of different joint models.
I am a fourth-year PhD student in Computer Science in Cornell University. My research interests are natural language processing and machine learning. Please visit my website to know more about me: https://sites.google.com/site/bishanyang/