Joint extraction of entities and relations for opinion recognition
Published 1 January 2006Open access
Yejin Choi, Eric Breck, Claire Cardie
Citations213
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TL;DR
An integer linear programming approach is employed to solve the joint opinion recognition task, and it is shown that global, constraint-based inference can significantly boost the performance of both relation extraction and the extraction of opinion-related entities.
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.
Keywords
Computer Science
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