@inproceedings{zhuang-etal-2022-exploiting,
title = "Exploiting Unary Relations with Stacked Learning for Relation Extraction",
author = "Zhuang, Yuan and
Riloff, Ellen and
Wagstaff, Kiri L. and
Francis, Raymond and
Golombek, Matthew P. and
Tamppari, Leslie K.",
editor = "Cohan, Arman and
Feigenblat, Guy and
Freitag, Dayne and
Ghosal, Tirthankar and
Herrmannova, Drahomira and
Knoth, Petr and
Lo, Kyle and
Mayr, Philipp and
Shmueli-Scheuer, Michal and
de Waard, Anita and
Wang, Lucy Lu",
booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sdp-1.14",
pages = "126--137",
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.",
}
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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.
%Y Cohan, Arman
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Herrmannova, Drahomira
%Y Knoth, Petr
%Y Lo, Kyle
%Y Mayr, Philipp
%Y Shmueli-Scheuer, Michal
%Y de Waard, Anita
%Y Wang, Lucy Lu
%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)
ACL