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        <title>Affective Event Classification with Discourse-enhanced Self-training</title>
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        <namePart type="given">Yuan</namePart>
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    <abstract>Prior research has recognized the need to associate affective polarities with events and has produced several techniques and lexical resources for identifying affective events. Our research introduces new classification models to assign affective polarity to event phrases. First, we present a BERT-based model for affective event classification and show that the classifier achieves substantially better performance than a large affective event knowledge base. Second, we present a discourse-enhanced self-training method that iteratively improves the classifier with unlabeled data. The key idea is to exploit event phrases that occur with a coreferent sentiment expression. The discourse-enhanced self-training algorithm iteratively labels new event phrases based on both the classifier’s predictions and the polarities of the event’s coreferent sentiment expressions. Our results show that discourse-enhanced self-training further improves both recall and precision for affective event classification.</abstract>
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%0 Conference Proceedings
%T Affective Event Classification with Discourse-enhanced Self-training
%A Zhuang, Yuan
%A Jiang, Tianyu
%A Riloff, Ellen
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhuang-etal-2020-affective
%X Prior research has recognized the need to associate affective polarities with events and has produced several techniques and lexical resources for identifying affective events. Our research introduces new classification models to assign affective polarity to event phrases. First, we present a BERT-based model for affective event classification and show that the classifier achieves substantially better performance than a large affective event knowledge base. Second, we present a discourse-enhanced self-training method that iteratively improves the classifier with unlabeled data. The key idea is to exploit event phrases that occur with a coreferent sentiment expression. The discourse-enhanced self-training algorithm iteratively labels new event phrases based on both the classifier’s predictions and the polarities of the event’s coreferent sentiment expressions. Our results show that discourse-enhanced self-training further improves both recall and precision for affective event classification.
%R 10.18653/v1/2020.emnlp-main.452
%U https://aclanthology.org/2020.emnlp-main.452
%U https://doi.org/10.18653/v1/2020.emnlp-main.452
%P 5608-5617

Markdown (Informal)

[Affective Event Classification with Discourse-enhanced Self-training](https://aclanthology.org/2020.emnlp-main.452) (Zhuang et al., EMNLP 2020)

• Affective Event Classification with Discourse-enhanced Self-training (Zhuang et al., EMNLP 2020)

ACL

• Yuan Zhuang, Tianyu Jiang, and Ellen Riloff. 2020. Affective Event Classification with Discourse-enhanced Self-training. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5608–5617, Online. Association for Computational Linguistics.