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        <title>Open Relation and Event Type Discovery with Type Abstraction</title>
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    <abstract>Conventional “closed-world” information extraction (IE) approaches rely on human ontologies to define the scope for extraction. As a result, such approaches fall short when applied to new domains. This calls for systems that can automatically infer new types from given corpora, a task which we refer to as type discovery. To tackle this problem, we introduce the idea of type abstraction, where the model is prompted to generalize and name the type. Then we use the similarity between inferred names to induce clusters. Observing that this abstraction-based representation is often complementary to the entity/trigger token representation, we set up these two representations as two views and design our model as a co-training framework. Our experiments on multiple relation extraction and event extraction datasets consistently show the advantage of our type abstraction approach.</abstract>
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%0 Conference Proceedings
%T Open Relation and Event Type Discovery with Type Abstraction
%A Li, Sha
%A Ji, Heng
%A Han, Jiawei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-etal-2022-open
%X Conventional “closed-world” information extraction (IE) approaches rely on human ontologies to define the scope for extraction. As a result, such approaches fall short when applied to new domains. This calls for systems that can automatically infer new types from given corpora, a task which we refer to as type discovery. To tackle this problem, we introduce the idea of type abstraction, where the model is prompted to generalize and name the type. Then we use the similarity between inferred names to induce clusters. Observing that this abstraction-based representation is often complementary to the entity/trigger token representation, we set up these two representations as two views and design our model as a co-training framework. Our experiments on multiple relation extraction and event extraction datasets consistently show the advantage of our type abstraction approach.
%R 10.18653/v1/2022.emnlp-main.461
%U https://aclanthology.org/2022.emnlp-main.461
%U https://doi.org/10.18653/v1/2022.emnlp-main.461
%P 6864-6877

Markdown (Informal)

[Open Relation and Event Type Discovery with Type Abstraction](https://aclanthology.org/2022.emnlp-main.461) (Li et al., EMNLP 2022)

• Open Relation and Event Type Discovery with Type Abstraction (Li et al., EMNLP 2022)

ACL

• Sha Li, Heng Ji, and Jiawei Han. 2022. Open Relation and Event Type Discovery with Type Abstraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6864–6877, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.