Papers by Binhan Yang

2 papers
Unveiling the Unknown: Open-Set Entity Typing via Two-Stage Generation (2026.acl-long)

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Challenge: Existing fine-grained entity typing models are susceptible to misclassify unknown-type instances . manual collection and annotation of large unknown-Type instances is time-consuming and labor-intensive in open environments.
Approach: They propose a novel task that uses open-set entity typing to classify entities of unknown types . they propose 'two-stage generation model' that automatically produces large-scale pseudo unknown-type instances .
Outcome: The proposed framework surpasses baselines on two newly established benchmark datasets.
A Triple-View Framework for Fine-Grained Emotion Classification with Clustering-Guided Contrastive Learning (2025.acl-long)

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Challenge: Existing studies have focused on dealing with only one of the two difficulties of coarse-grained emotion classification.
Approach: They propose a triple-view framework that treats FEC as an instance-label joint embedding learning problem to tackle both difficulties concurrently by considering three complementary views.
Outcome: The proposed framework achieves significant and consistent improvements on two widely-used benchmark datasets.

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