Joint Extraction of Entities and Relations for Opinion Recognition

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 431–439, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Joint Extraction of Entities and Relations for Opinion Recognition Yejin Choi and Eric Breck and Claire Cardie Department of Computer Science Cornell University Ithaca, NY 14853 {ychoi,ebreck,cardie}@cs.cornell.edu Abstract We present an approach for the joint ex- traction of entities and relations in the con- text of opinion recognition and analysis. We identify two types of opinion-related entities — expressions of opinions and sources of opinions — along with the link- ing relation that exists between them. In- spired 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 in- ference can significantly boost the perfor- mance of both relation extraction and the extraction of opinion-related entities. Per- formance further improves when a seman- tic role labeling system is incorporated. The resulting system achieves F-measures of 79 and 69 for entity and relation extrac- tion, respectively, improving substantially over prior results in the area. 1 Introduction Information extraction tasks such as recognizing entities and relations have long been considered critical to many domain-specific NLP tasks (e.g. Mooney and Bunescu (2005), Prager et al. (2000), White et al. (2001)). Researchers have further shown that opinion-oriented information extrac- tion can provide analogous benefits to a variety of practical applications including product reputation tracking (Morinaga et al., 2002), opinion-oriented question answering (Stoyanov et al., 2005), and opinion-oriented summarization (e.g. Cardie et al. (2004), Liu et al. (2005)). Moreover, much progress has been made in the area of opinion ex- traction: it is possible to identify sources of opin- ions (i.e. the opinion holders) (e.g. Choi et al. (2005) and Kim and Hovy (2005b)), to determine the polarity and strength of opinion expressions (e.g. Wilson et al. (2005)), and to recognize propo- sitional opinions and their sources (e.g. Bethard et al. (2004)) with reasonable accuracy. To date, however, there has been no effort to simultane- ously identify arbitrary opinion expressions, their sources, and the relations between them. Without progress on the joint extraction of opinion enti- ties and their relations, the capabilities of opinion- based applications will remain limited. Fortunately, research in machine learning has produced methods for global inference and joint classification that can help to address this defi- ciency (e.g. Bunescu and Mooney (2004), Roth and Yih (2004)). Moreover, it has been shown that exploiting dependencies among entities and/or re- lations via global inference not only solves the joint extraction task, but often boosts performance on the individual tasks when compared to clas- sifiers that handle the tasks independently — for semantic role labeling (e.g. Punyakanok et al. (2004)), information extraction (e.g. Roth and Yih (2004)), and sequence tagging (e.g. Sutton et al. (2004)). In this paper, we present a global inference ap- proach (Roth and Yih, 2004) to the extraction of opinion-related entities and relations. In par- ticular, we aim to identify two types …

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