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Comparing neural models for nested and overlapping biomedical event detection
BACKGROUND: Nested and overlapping events are particularly frequent and informative structures in biomedical event extraction. However, state-of-the-art neural models either neglect those structures during learning or use syntactic features and external tools to detect them. To overcome these limita...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9161617/ https://www.ncbi.nlm.nih.gov/pubmed/35655127 http://dx.doi.org/10.1186/s12859-022-04746-3 |
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author | Espinosa, Kurt Georgiadis, Panagiotis Christopoulou, Fenia Ju, Meizhi Miwa, Makoto Ananiadou, Sophia |
author_facet | Espinosa, Kurt Georgiadis, Panagiotis Christopoulou, Fenia Ju, Meizhi Miwa, Makoto Ananiadou, Sophia |
author_sort | Espinosa, Kurt |
collection | PubMed |
description | BACKGROUND: Nested and overlapping events are particularly frequent and informative structures in biomedical event extraction. However, state-of-the-art neural models either neglect those structures during learning or use syntactic features and external tools to detect them. To overcome these limitations, this paper presents and compares two neural models: a novel EXhaustive Neural Network (EXNN) and a Search-Based Neural Network (SBNN) for detection of nested and overlapping events. RESULTS: We evaluate the proposed models as an event detection component in isolation and within a pipeline setting. Evaluation in several annotated biomedical event extraction datasets shows that both EXNN and SBNN achieve higher performance in detecting nested and overlapping events, compared to the state-of-the-art model Turku Event Extraction System (TEES). CONCLUSIONS: The experimental results reveal that both EXNN and SBNN are effective for biomedical event extraction. Furthermore, results on a pipeline setting indicate that our models improve detection of events compared to models that use either gold or predicted named entities. |
format | Online Article Text |
id | pubmed-9161617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91616172022-06-03 Comparing neural models for nested and overlapping biomedical event detection Espinosa, Kurt Georgiadis, Panagiotis Christopoulou, Fenia Ju, Meizhi Miwa, Makoto Ananiadou, Sophia BMC Bioinformatics Research BACKGROUND: Nested and overlapping events are particularly frequent and informative structures in biomedical event extraction. However, state-of-the-art neural models either neglect those structures during learning or use syntactic features and external tools to detect them. To overcome these limitations, this paper presents and compares two neural models: a novel EXhaustive Neural Network (EXNN) and a Search-Based Neural Network (SBNN) for detection of nested and overlapping events. RESULTS: We evaluate the proposed models as an event detection component in isolation and within a pipeline setting. Evaluation in several annotated biomedical event extraction datasets shows that both EXNN and SBNN achieve higher performance in detecting nested and overlapping events, compared to the state-of-the-art model Turku Event Extraction System (TEES). CONCLUSIONS: The experimental results reveal that both EXNN and SBNN are effective for biomedical event extraction. Furthermore, results on a pipeline setting indicate that our models improve detection of events compared to models that use either gold or predicted named entities. BioMed Central 2022-06-02 /pmc/articles/PMC9161617/ /pubmed/35655127 http://dx.doi.org/10.1186/s12859-022-04746-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Espinosa, Kurt Georgiadis, Panagiotis Christopoulou, Fenia Ju, Meizhi Miwa, Makoto Ananiadou, Sophia Comparing neural models for nested and overlapping biomedical event detection |
title | Comparing neural models for nested and overlapping biomedical event detection |
title_full | Comparing neural models for nested and overlapping biomedical event detection |
title_fullStr | Comparing neural models for nested and overlapping biomedical event detection |
title_full_unstemmed | Comparing neural models for nested and overlapping biomedical event detection |
title_short | Comparing neural models for nested and overlapping biomedical event detection |
title_sort | comparing neural models for nested and overlapping biomedical event detection |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9161617/ https://www.ncbi.nlm.nih.gov/pubmed/35655127 http://dx.doi.org/10.1186/s12859-022-04746-3 |
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