Joint Modeling of Opinion Expression Extraction and Attribute Classification
Abstract
In 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.Author Biography
Bishan Yang
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/