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        <title>Exploiting Unary Relations with Stacked Learning for Relation Extraction</title>
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    <abstract>Relation extraction models typically cast the problem of determining whether there is a relation between a pair of entities as a single decision. However, these models can struggle with long or complex language constructions in which two entities are not directly linked, as is often the case in scientific publications. We propose a novel approach that decomposes a binary relation into two unary relations that capture each argument’s role in the relation separately. We create a stacked learning model that incorporates information from unary and binary relation extractors to determine whether a relation holds between two entities. We present experimental results showing that this approach outperforms several competitive relation extractors on a new corpus of planetary science publications as well as a benchmark dataset in the biology domain.</abstract>
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%0 Conference Proceedings
%T Exploiting Unary Relations with Stacked Learning for Relation Extraction
%A Zhuang, Yuan
%A Riloff, Ellen
%A Wagstaff, Kiri L.
%A Francis, Raymond
%A Golombek, Matthew P.
%A Tamppari, Leslie K.
%S Proceedings of the Third Workshop on Scholarly Document Processing
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F zhuang-etal-2022-exploiting
%X Relation extraction models typically cast the problem of determining whether there is a relation between a pair of entities as a single decision. However, these models can struggle with long or complex language constructions in which two entities are not directly linked, as is often the case in scientific publications. We propose a novel approach that decomposes a binary relation into two unary relations that capture each argument’s role in the relation separately. We create a stacked learning model that incorporates information from unary and binary relation extractors to determine whether a relation holds between two entities. We present experimental results showing that this approach outperforms several competitive relation extractors on a new corpus of planetary science publications as well as a benchmark dataset in the biology domain.
%U https://aclanthology.org/2022.sdp-1.14
%P 126-137

Markdown (Informal)

[Exploiting Unary Relations with Stacked Learning for Relation Extraction](https://aclanthology.org/2022.sdp-1.14) (Zhuang et al., sdp 2022)

• Exploiting Unary Relations with Stacked Learning for Relation Extraction (Zhuang et al., sdp 2022)

ACL

• Yuan Zhuang, Ellen Riloff, Kiri L. Wagstaff, Raymond Francis, Matthew P. Golombek, and Leslie K. Tamppari. 2022. Exploiting Unary Relations with Stacked Learning for Relation Extraction. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 126–137, Gyeongju, Republic of Korea. Association for Computational Linguistics.