Papers by Chaofa Yuan

3 papers
Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction (2020.acl-main)

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Challenge: Existing methods to extract emotions and causes from unannotated text are pipelined, causing error propagation.
Approach: They propose to transform a task into a procedure of parsing-like directed graph construction . they propose to generate a directed graph with labeled edges based on a sequence of actions .
Outcome: The proposed method outperforms the state-of-the-art methods by 6.71% (p0.01) in F1 measure.
Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme (2020.emnlp-main)

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Challenge: Existing methods to extract emotions and causes from unannotated emotion texts are labor intensive and limited applications in real-world scenarios.
Approach: They propose a novel task to find emotions and corresponding causes in unannotated emotion texts.
Outcome: The proposed model outperforms the state-of-the-art method by 2.26% (p0.001) in F1 measure.
Target-based Sentiment Annotation in Chinese Financial News (2020.lrec-1)

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Challenge: Using a large corpus of 8,314 target-level sentiment annotations, sentiment classification on multiple opinion aspects/targets level is unsatisfactory.
Approach: They propose to construct a large-scale target-based sentiment annotation corpus on Chinese financial news text.
Outcome: The proposed corpus has 8,314 target-level sentiment annotations on Chinese financial news text.

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