@inproceedings{el-khettari-etal-2024-mention,
title = "Mention-Agnostic Information Extraction for Ontological Annotation of Biomedical Articles",
author = "El Khettari, Oumaima and
Nishida, Noriki and
Liu, Shanshan and
Munne, Rumana Ferdous and
Yamagata, Yuki and
Quiniou, Solen and
Chaffron, Samuel and
Matsumoto, Yuji",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.37",
pages = "457--473",
abstract = "Biomedical information extraction is crucial for advancing research, enhancing healthcare, and discovering treatments by efficiently analyzing extensive data. Given the extensive amount of biomedical data available, automated information extraction methods are necessary due to manual extraction{'}s labor-intensive, expertise-dependent, and costly nature. In this paper, we propose a novel two-stage system for information extraction where we annotate biomedical articles based on a specific ontology (HOIP). The major challenge is annotating relation between biomedical processes often not explicitly mentioned in text articles. Here, we first predict the candidate processes and then determine the relationships between these processes. The experimental results show promising outcomes in mention-agnostic process identification using Large Language Models (LLMs). In relation classification, BERT-based supervised models still outperform LLMs significantly. The end-to-end evaluation results suggest the difficulty of this task and room for improvement in both process identification and relation classification.",
}
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<abstract>Biomedical information extraction is crucial for advancing research, enhancing healthcare, and discovering treatments by efficiently analyzing extensive data. Given the extensive amount of biomedical data available, automated information extraction methods are necessary due to manual extraction’s labor-intensive, expertise-dependent, and costly nature. In this paper, we propose a novel two-stage system for information extraction where we annotate biomedical articles based on a specific ontology (HOIP). The major challenge is annotating relation between biomedical processes often not explicitly mentioned in text articles. Here, we first predict the candidate processes and then determine the relationships between these processes. The experimental results show promising outcomes in mention-agnostic process identification using Large Language Models (LLMs). In relation classification, BERT-based supervised models still outperform LLMs significantly. The end-to-end evaluation results suggest the difficulty of this task and room for improvement in both process identification and relation classification.</abstract>
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%0 Conference Proceedings
%T Mention-Agnostic Information Extraction for Ontological Annotation of Biomedical Articles
%A El Khettari, Oumaima
%A Nishida, Noriki
%A Liu, Shanshan
%A Munne, Rumana Ferdous
%A Yamagata, Yuki
%A Quiniou, Solen
%A Chaffron, Samuel
%A Matsumoto, Yuji
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F el-khettari-etal-2024-mention
%X Biomedical information extraction is crucial for advancing research, enhancing healthcare, and discovering treatments by efficiently analyzing extensive data. Given the extensive amount of biomedical data available, automated information extraction methods are necessary due to manual extraction’s labor-intensive, expertise-dependent, and costly nature. In this paper, we propose a novel two-stage system for information extraction where we annotate biomedical articles based on a specific ontology (HOIP). The major challenge is annotating relation between biomedical processes often not explicitly mentioned in text articles. Here, we first predict the candidate processes and then determine the relationships between these processes. The experimental results show promising outcomes in mention-agnostic process identification using Large Language Models (LLMs). In relation classification, BERT-based supervised models still outperform LLMs significantly. The end-to-end evaluation results suggest the difficulty of this task and room for improvement in both process identification and relation classification.
%U https://aclanthology.org/2024.bionlp-1.37
%P 457-473
Markdown (Informal)
[Mention-Agnostic Information Extraction for Ontological Annotation of Biomedical Articles](https://aclanthology.org/2024.bionlp-1.37) (El Khettari et al., BioNLP-WS 2024)
ACL
- Oumaima El Khettari, Noriki Nishida, Shanshan Liu, Rumana Ferdous Munne, Yuki Yamagata, Solen Quiniou, Samuel Chaffron, and Yuji Matsumoto. 2024. Mention-Agnostic Information Extraction for Ontological Annotation of Biomedical Articles. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 457–473, Bangkok, Thailand. Association for Computational Linguistics.