Detection of fields of applications in biomedical abstracts with the support of argumentation elements
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916198402228224 |
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| author | Neves, Mariana |
| author_facet | Neves, Mariana |
| contents | Focusing on particular facts, instead of the complete text, can potentially improve searching for specific information in the scientific literature. In particular, argumentative elements allow focusing on specific parts of a publication, e.g., the background section or the claims from the authors. We evaluated some tools for the extraction of argumentation elements for a specific task in biomedicine, namely, for detecting the fields of the application in a biomedical publication, e.g, whether it addresses the problem of disease diagnosis or drug development. We performed experiments with the PubMedBERT pre-trained model, which was fine-tuned on a specific corpus for the task. We compared the use of title and abstract to restricting to only some argumentative elements. The top F1 scores ranged from 0.22 to 0.84, depending on the field of application. The best argumentative labels were the ones related the conclusion and background sections of an abstract. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06121 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detection of fields of applications in biomedical abstracts with the support of argumentation elements Neves, Mariana Computation and Language Focusing on particular facts, instead of the complete text, can potentially improve searching for specific information in the scientific literature. In particular, argumentative elements allow focusing on specific parts of a publication, e.g., the background section or the claims from the authors. We evaluated some tools for the extraction of argumentation elements for a specific task in biomedicine, namely, for detecting the fields of the application in a biomedical publication, e.g, whether it addresses the problem of disease diagnosis or drug development. We performed experiments with the PubMedBERT pre-trained model, which was fine-tuned on a specific corpus for the task. We compared the use of title and abstract to restricting to only some argumentative elements. The top F1 scores ranged from 0.22 to 0.84, depending on the field of application. The best argumentative labels were the ones related the conclusion and background sections of an abstract. |
| title | Detection of fields of applications in biomedical abstracts with the support of argumentation elements |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.06121 |