@inproceedings{amin-etal-2020-data,
title = "A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction",
author = "Amin, Saadullah and
Dunfield, Katherine Ann and
Vechkaeva, Anna and
Neumann, Guenter",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.bionlp-1.20",
doi = "10.18653/v1/2020.bionlp-1.20",
pages = "187--194",
abstract = "Fact triples are a common form of structured knowledge used within the biomedical domain. As the amount of unstructured scientific texts continues to grow, manual annotation of these texts for the task of relation extraction becomes increasingly expensive. Distant supervision offers a viable approach to combat this by quickly producing large amounts of labeled, but considerably noisy, data. We aim to reduce such noise by extending an entity-enriched relation classification BERT model to the problem of multiple instance learning, and defining a simple data encoding scheme that significantly reduces noise, reaching state-of-the-art performance for distantly-supervised biomedical relation extraction. Our approach further encodes knowledge about the direction of relation triples, allowing for increased focus on relation learning by reducing noise and alleviating the need for joint learning with knowledge graph completion.",
}
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<abstract>Fact triples are a common form of structured knowledge used within the biomedical domain. As the amount of unstructured scientific texts continues to grow, manual annotation of these texts for the task of relation extraction becomes increasingly expensive. Distant supervision offers a viable approach to combat this by quickly producing large amounts of labeled, but considerably noisy, data. We aim to reduce such noise by extending an entity-enriched relation classification BERT model to the problem of multiple instance learning, and defining a simple data encoding scheme that significantly reduces noise, reaching state-of-the-art performance for distantly-supervised biomedical relation extraction. Our approach further encodes knowledge about the direction of relation triples, allowing for increased focus on relation learning by reducing noise and alleviating the need for joint learning with knowledge graph completion.</abstract>
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%0 Conference Proceedings
%T A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction
%A Amin, Saadullah
%A Dunfield, Katherine Ann
%A Vechkaeva, Anna
%A Neumann, Guenter
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F amin-etal-2020-data
%X Fact triples are a common form of structured knowledge used within the biomedical domain. As the amount of unstructured scientific texts continues to grow, manual annotation of these texts for the task of relation extraction becomes increasingly expensive. Distant supervision offers a viable approach to combat this by quickly producing large amounts of labeled, but considerably noisy, data. We aim to reduce such noise by extending an entity-enriched relation classification BERT model to the problem of multiple instance learning, and defining a simple data encoding scheme that significantly reduces noise, reaching state-of-the-art performance for distantly-supervised biomedical relation extraction. Our approach further encodes knowledge about the direction of relation triples, allowing for increased focus on relation learning by reducing noise and alleviating the need for joint learning with knowledge graph completion.
%R 10.18653/v1/2020.bionlp-1.20
%U https://aclanthology.org/2020.bionlp-1.20
%U https://doi.org/10.18653/v1/2020.bionlp-1.20
%P 187-194
Markdown (Informal)
[A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction](https://aclanthology.org/2020.bionlp-1.20) (Amin et al., BioNLP 2020)
ACL