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.
Xiaowen Ding Bing Liu
Department of Computer ScienceUniversity of Illinois at Chicago Department of Computer Science University of Illinois at Chicago
国际会议
The 23rd International Conference on Computational Linguistics(第23届国际计算语言学大会)
北京
英文
268-276
2010-08-01（万方平台首次上网日期，不代表论文的发表时间）