2006 JointExtrOfEntsAndRelsForOpinionRecog

(Choi et al., 2006) ⇒ Yejin Choi, Eric Breck, and Claire Cardie. (2006). “Joint Extraction of Entities and Relations for Opinion Recognition.” In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP 2006).

Subject Headings: Relation Extraction from Text Algorithm

Notes

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Abstract

We present an approach for the joint extraction of entities and relations in the context of opinion recognition and analysis. We identify two types of opinion-related entities — expressions of opinions and sources of opinions — along with the linking relation that exists between them. Inspired by Roth and Yih (2004), we employ an integer linear programming approach to solve the joint opinion recognition task, and show that global, constraint-based inference can significantly boost the performance of both relation extraction and the extraction of opinion-related entities. Performance further improves when a semantic role labeling system is incorporated. The resulting system achieves F-measures of 79 and 69 for entity and relation extraction, respectively, improving substantially over prior results in the area.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 JointExtrOfEntsAndRelsForOpinionRecog	Yejin Choi Eric Breck Claire Cardie			Joint Extraction of Entities and Relations for Opinion Recognition		Proceedings of Empirical Methods in Natural Language Processing	http://www.cs.cornell.edu/home/cardie/papers/emnlp06-yejin.pdf			2006