Advancing Relation Extraction through Language Probing with Exemplars from Set Co-Expansion

• URL: http://arxiv.org/abs/2308.11720v1
• Date: Fri, 18 Aug 2023 00:56:35 GMT
• Title: Advancing Relation Extraction through Language Probing with Exemplars from Set Co-Expansion
• Authors: Yerong Li, Roxana Girju
• Abstract summary: Relation Extraction (RE) is a pivotal task in automatically extracting structured information from unstructured text. We present a multi-faceted approach that integrates representative examples and through co-set expansion. Our method achieves an observed margin of at least 1 percent improvement in accuracy in most settings.
• Score: 1.450405446885067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Relation Extraction (RE) is a pivotal task in automatically extracting structured information from unstructured text. In this paper, we present a multi-faceted approach that integrates representative examples and through co-set expansion. The primary goal of our method is to enhance relation classification accuracy and mitigating confusion between contrastive classes. Our approach begins by seeding each relationship class with representative examples. Subsequently, our co-set expansion algorithm enriches training objectives by incorporating similarity measures between target pairs and representative pairs from the target class. Moreover, the co-set expansion process involves a class ranking procedure that takes into account exemplars from contrastive classes. Contextual details encompassing relation mentions are harnessed via context-free Hearst patterns to ascertain contextual similarity. Empirical evaluation demonstrates the efficacy of our co-set expansion approach, resulting in a significant enhancement of relation classification performance. Our method achieves an observed margin of at least 1 percent improvement in accuracy in most settings, on top of existing fine-tuning approaches. To further refine our approach, we conduct an in-depth analysis that focuses on tuning contrastive examples. This strategic selection and tuning effectively reduce confusion between classes sharing similarities, leading to a more precise classification process. Experimental results underscore the effectiveness of our proposed framework for relation extraction. The synergy between co-set expansion and context-aware prompt tuning substantially contributes to improved classification accuracy. Furthermore, the reduction in confusion between contrastive classes through contrastive examples tuning validates the robustness and reliability of our method.

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