Exploiting Background Knowledge for Relation Extraction
Relation extraction is the task of recognizing semantic relations among entities. Given a particular sentence supervised approaches to Relation Extraction employed feature or kernel functions which usually have a single sentence in their scope. The overall aim of this paper is to propose methods for using knowledge and resources that are external to the target sentence, as a way to improve relation extraction. We demonstrate this by exploiting background knowledge such as relationships among the target relations, as well as by considering how target relations relate to some existing knowledge resources. Our methods are general and we suggest that some of them could be applied to other NLP tasks.
Yee Seng Chan Dan Roth
University of Illinois at Urbana-Champaign
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
152-160
2010-08-01（万方平台首次上网日期，不代表论文的发表时间）