Yifan Yang


2022

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HITSZ-HLT at SemEval-2022 Task 10: A Span-Relation Extraction Framework for Structured Sentiment Analysis
Yihui Li | Yifan Yang | Yice Zhang | Ruifeng Xu
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper describes our system that participated in the SemEval-2022 Task 10: Structured Sentiment Analysis, which aims to extract opinion tuples from texts.A full opinion tuple generally contains an opinion holder, an opinion target, the sentiment expression, and the corresponding polarity.The complex structure of the opinion tuple makes the task challenging.To address this task, we formalize it as a span-relation extraction problem and propose a two-stage extraction framework accordingly.In the first stage, we employ the span module to enumerate spans and then recognize the type of every span.In the second stage, we employ the relation module to determine the relation between spans.Our system achieves competitive results and ranks among the top-10 systems in almost subtasks.

2021

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PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Hengyi Zheng | Rui Wen | Xi Chen | Yifan Yang | Yunyan Zhang | Ziheng Zhang | Ningyu Zhang | Bin Qin | Xu Ming | Yefeng Zheng
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples. The source code has been submitted as the supplementary material and will be made publicly available after the blind review.