Exploiting Unary Relations with Stacked Learning for Relation Extraction

Yuan Zhuang, Ellen Riloff, Kiri L. Wagstaff, Raymond Francis, Matthew P. Golombek, Leslie K. Tamppari


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.
Anthology ID:
2022.sdp-1.14
Volume:
Proceedings of the Third Workshop on Scholarly Document Processing
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
126–137
Language:
URL:
https://aclanthology.org/2022.sdp-1.14
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Exploiting Unary Relations with Stacked Learning for Relation Extraction (Zhuang et al., sdp 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.sdp-1.14.pdf
Code
 yyzhuang1991/stackedlearningwithunarymodels
Data
LPSC