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BioASQ-QA: A manually curated corpus for Biomedical Question Answering
The BioASQ question answering (QA) benchmark dataset contains questions in English, along with golden standard (reference) answers and related material. The dataset has been designed to reflect real information needs of biomedical experts and is therefore more realistic and challenging than most exi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042099/ https://www.ncbi.nlm.nih.gov/pubmed/36973320 http://dx.doi.org/10.1038/s41597-023-02068-4 |
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author | Krithara, Anastasia Nentidis, Anastasios Bougiatiotis, Konstantinos Paliouras, Georgios |
author_facet | Krithara, Anastasia Nentidis, Anastasios Bougiatiotis, Konstantinos Paliouras, Georgios |
author_sort | Krithara, Anastasia |
collection | PubMed |
description | The BioASQ question answering (QA) benchmark dataset contains questions in English, along with golden standard (reference) answers and related material. The dataset has been designed to reflect real information needs of biomedical experts and is therefore more realistic and challenging than most existing datasets. Furthermore, unlike most previous QA benchmarks that contain only exact answers, the BioASQ-QA dataset also includes ideal answers (in effect summaries), which are particularly useful for research on multi-document summarization. The dataset combines structured and unstructured data. The materials linked with each question comprise documents and snippets, which are useful for Information Retrieval and Passage Retrieval experiments, as well as concepts that are useful in concept-to-text Natural Language Generation. Researchers working on paraphrasing and textual entailment can also measure the degree to which their methods improve the performance of biomedical QA systems. Last but not least, the dataset is continuously extended, as the BioASQ challenge is running and new data are generated. |
format | Online Article Text |
id | pubmed-10042099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100420992023-03-28 BioASQ-QA: A manually curated corpus for Biomedical Question Answering Krithara, Anastasia Nentidis, Anastasios Bougiatiotis, Konstantinos Paliouras, Georgios Sci Data Data Descriptor The BioASQ question answering (QA) benchmark dataset contains questions in English, along with golden standard (reference) answers and related material. The dataset has been designed to reflect real information needs of biomedical experts and is therefore more realistic and challenging than most existing datasets. Furthermore, unlike most previous QA benchmarks that contain only exact answers, the BioASQ-QA dataset also includes ideal answers (in effect summaries), which are particularly useful for research on multi-document summarization. The dataset combines structured and unstructured data. The materials linked with each question comprise documents and snippets, which are useful for Information Retrieval and Passage Retrieval experiments, as well as concepts that are useful in concept-to-text Natural Language Generation. Researchers working on paraphrasing and textual entailment can also measure the degree to which their methods improve the performance of biomedical QA systems. Last but not least, the dataset is continuously extended, as the BioASQ challenge is running and new data are generated. Nature Publishing Group UK 2023-03-27 /pmc/articles/PMC10042099/ /pubmed/36973320 http://dx.doi.org/10.1038/s41597-023-02068-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Krithara, Anastasia Nentidis, Anastasios Bougiatiotis, Konstantinos Paliouras, Georgios BioASQ-QA: A manually curated corpus for Biomedical Question Answering |
title | BioASQ-QA: A manually curated corpus for Biomedical Question Answering |
title_full | BioASQ-QA: A manually curated corpus for Biomedical Question Answering |
title_fullStr | BioASQ-QA: A manually curated corpus for Biomedical Question Answering |
title_full_unstemmed | BioASQ-QA: A manually curated corpus for Biomedical Question Answering |
title_short | BioASQ-QA: A manually curated corpus for Biomedical Question Answering |
title_sort | bioasq-qa: a manually curated corpus for biomedical question answering |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042099/ https://www.ncbi.nlm.nih.gov/pubmed/36973320 http://dx.doi.org/10.1038/s41597-023-02068-4 |
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