Chaofa Yuan


2020

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Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction
Chuang Fan | Chaofa Yuan | Jiachen Du | Lin Gui | Min Yang | Ruifeng Xu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p<0.01) in F1 measure.

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Target-based Sentiment Annotation in Chinese Financial News
Chaofa Yuan | Yuhan Liu | Rongdi Yin | Jun Zhang | Qinling Zhu | Ruibin Mao | Ruifeng Xu
Proceedings of the 12th Language Resources and Evaluation Conference

This paper presents the design and construction of a large-scale target-based sentiment annotation corpus on Chinese financial news text. Different from the most existing paragraph/document-based annotation corpus, in this study, target-based fine-grained sentiment annotation is performed. The companies, brands and other financial entities are regarded as the targets. The clause reflecting the profitability, loss or other business status of financial entities is regarded as the sentiment expression for determining the polarity. Based on high quality annotation guideline and effective quality control strategy, a corpus with 8,314 target-level sentiment annotation is constructed on 6,336 paragraphs from Chinese financial news text. Based on this corpus, several state-of-the-art sentiment analysis models are evaluated.

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Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme
Chaofa Yuan | Chuang Fan | Jianzhu Bao | Ruifeng Xu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The task of emotion-cause pair extraction deals with finding all emotions and the corresponding causes in unannotated emotion texts. Most recent studies are based on the likelihood of Cartesian product among all clause candidates, resulting in a high computational cost. Targeting this issue, we regard the task as a sequence labeling problem and propose a novel tagging scheme with coding the distance between linked components into the tags, so that emotions and the corresponding causes can be extracted simultaneously. Accordingly, an end-to-end model is presented to process the input texts from left to right, always with linear time complexity, leading to a speed up. Experimental results show that our proposed model achieves the best performance, outperforming the state-of-the-art method by 2.26% (p<0.001) in F1 measure.