Resolving object and attribute coreference in opinion mining

Coreference resolution is a classic NLP problem and has been studied extensively by many researchers. Most existing studies, however, are generic in the sense that they are not focused on any specific text. In the past few years, opinion mining became a popular topic of research because of a wide range of applications. However, limited work has been done on coreference resolution in opinionated text. In this paper, we deal with object and attribute coreference resolution. Such coreference resolutions are important because without solving it a great deal of opinion information will be lost, and opinions may be assigned to wrong entities. We show that some important features related to opinions can be exploited to perform the task more accurately. Experimental results using blog posts demonstrate the effectiveness of the technique.