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        <title>Learning to Classify Events from Human Needs Category Descriptions</title>
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    <abstract>We study the problem of learning an event classifier from human needs category descriptions, which is challenging due to: (1) the use of highly abstract concepts in natural language descriptions, (2) the difficulty of choosing key concepts. To tackle these two challenges, we propose LeaPI, a zero-shot learning method that first automatically generate weak labels by instantiating high-level concepts with prototypical instances and then trains a human needs classifier with the weakly labeled data. To filter noisy concepts, we design a reinforced selection algorithm to choose high-quality concepts for instantiation. Experimental results on the human needs categorization task show that our method outperforms baseline methods, producing substantially better precision.</abstract>
    <identifier type="citekey">ding-feng-2020-learning</identifier>
    <identifier type="doi">10.18653/v1/2020.findings-emnlp.421</identifier>
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%0 Conference Proceedings
%T Learning to Classify Events from Human Needs Category Descriptions
%A Ding, Haibo
%A Feng, Zhe
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ding-feng-2020-learning
%X We study the problem of learning an event classifier from human needs category descriptions, which is challenging due to: (1) the use of highly abstract concepts in natural language descriptions, (2) the difficulty of choosing key concepts. To tackle these two challenges, we propose LeaPI, a zero-shot learning method that first automatically generate weak labels by instantiating high-level concepts with prototypical instances and then trains a human needs classifier with the weakly labeled data. To filter noisy concepts, we design a reinforced selection algorithm to choose high-quality concepts for instantiation. Experimental results on the human needs categorization task show that our method outperforms baseline methods, producing substantially better precision.
%R 10.18653/v1/2020.findings-emnlp.421
%U https://aclanthology.org/2020.findings-emnlp.421
%U https://doi.org/10.18653/v1/2020.findings-emnlp.421
%P 4698-4704

Markdown (Informal)

[Learning to Classify Events from Human Needs Category Descriptions](https://aclanthology.org/2020.findings-emnlp.421) (Ding & Feng, Findings 2020)

• Learning to Classify Events from Human Needs Category Descriptions (Ding & Feng, Findings 2020)

ACL

• Haibo Ding and Zhe Feng. 2020. Learning to Classify Events from Human Needs Category Descriptions. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4698–4704, Online. Association for Computational Linguistics.