• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

BioASQ-QA：用于生物医学问答的人工策论文本语料库。

BioASQ-QA: A manually curated corpus for Biomedical Question Answering.

机构信息

Institute of Informatics and Telecommunications, National Center for Scientific Research "Demokritos", Athens, Greece.

School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.

出版信息

Sci Data. 2023 Mar 27;10(1):170. doi: 10.1038/s41597-023-02068-4.

DOI:10.1038/s41597-023-02068-4
PMID:36973320
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10042099/
Abstract

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.

摘要

BioASQ 问答 (QA) 基准数据集包含英文问题，以及黄金标准 (参考) 答案和相关材料。该数据集旨在反映生物医学专家的实际信息需求，因此比大多数现有数据集更具现实性和挑战性。此外，与大多数仅包含确切答案的以前的 QA 基准不同，BioASQ-QA 数据集还包括理想答案 (实际上是摘要)，这对于多文档摘要研究特别有用。该数据集结合了结构化和非结构化数据。每个问题链接的材料包括文档和片段，这对于信息检索和段落检索实验以及在概念到文本的自然语言生成中有用的概念非常有用。从事释义和文本蕴涵研究的研究人员也可以衡量他们的方法在多大程度上提高了生物医学 QA 系统的性能。最后但同样重要的是，随着 BioASQ 挑战赛的进行和新数据的生成，该数据集在不断扩展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/c20c1a6de20e/41597_2023_2068_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/e4f45823cd2e/41597_2023_2068_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/569ff4ab771c/41597_2023_2068_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/734fbd60580b/41597_2023_2068_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/f8a28a89f158/41597_2023_2068_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/8518ab87ae89/41597_2023_2068_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/b61847efe9ba/41597_2023_2068_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/984e2e32b7e6/41597_2023_2068_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/4b1c371b176a/41597_2023_2068_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/2a8cc270479f/41597_2023_2068_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/161a90c6c3ab/41597_2023_2068_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/c9f71f015943/41597_2023_2068_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/c20c1a6de20e/41597_2023_2068_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/e4f45823cd2e/41597_2023_2068_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/569ff4ab771c/41597_2023_2068_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/734fbd60580b/41597_2023_2068_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/f8a28a89f158/41597_2023_2068_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/8518ab87ae89/41597_2023_2068_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/b61847efe9ba/41597_2023_2068_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/984e2e32b7e6/41597_2023_2068_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/4b1c371b176a/41597_2023_2068_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/2a8cc270479f/41597_2023_2068_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/161a90c6c3ab/41597_2023_2068_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/c9f71f015943/41597_2023_2068_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8861/10042805/c20c1a6de20e/41597_2023_2068_Fig12_HTML.jpg

相似文献

1
BioASQ-QA: A manually curated corpus for Biomedical Question Answering.BioASQ-QA：用于生物医学问答的人工策论文本语料库。
Sci Data. 2023 Mar 27;10(1):170. doi: 10.1038/s41597-023-02068-4.
2
An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition.BIOASQ大规模生物医学语义索引与问答竞赛概述。
BMC Bioinformatics. 2015 Apr 30;16:138. doi: 10.1186/s12859-015-0564-6.
3
SemBioNLQA: A semantic biomedical question answering system for retrieving exact and ideal answers to natural language questions.SemBioNLQA：一个语义生物医学问答系统，用于检索自然语言问题的准确和理想答案。
Artif Intell Med. 2020 Jan;102:101767. doi: 10.1016/j.artmed.2019.101767. Epub 2019 Nov 28.
4
Document Retrieval for Biomedical Question Answering with Neural Sentence Matching.用于生物医学问答的神经句子匹配文档检索
Proc Int Conf Mach Learn Appl. 2018 Dec;2018:194-201. doi: 10.1109/ICMLA.2018.00036. Epub 2019 Jan 17.
5
A passage retrieval method based on probabilistic information retrieval model and UMLS concepts in biomedical question answering.一种基于概率信息检索模型和统一医学语言系统（UMLS）概念的生物医学问答中的段落检索方法。
J Biomed Inform. 2017 Apr;68:96-103. doi: 10.1016/j.jbi.2017.03.001. Epub 2017 Mar 7.
6
BioASQ Synergy: a dialogue between question-answering systems and biomedical experts for promoting COVID-19 research.BioASQ 协同：问答系统与生物医学专家之间的对话，以促进 COVID-19 研究。
J Am Med Inform Assoc. 2024 Nov 1;31(11):2689-2698. doi: 10.1093/jamia/ocae232.
7
Word embeddings and external resources for answer processing in biomedical factoid question answering.词向量和外部资源在生物医学事实问答中的答案处理
J Biomed Inform. 2019 Apr;92:103118. doi: 10.1016/j.jbi.2019.103118. Epub 2019 Feb 10.
8
A question-entailment approach to question answering.问题蕴涵方法在问答中的应用。
BMC Bioinformatics. 2019 Oct 22;20(1):511. doi: 10.1186/s12859-019-3119-4.
9
External features enriched model for biomedical question answering.生物医学问答的外部特征丰富模型。
BMC Bioinformatics. 2021 May 26;22(1):272. doi: 10.1186/s12859-021-04176-7.
10
Epidemic Question Answering: question generation and entailment for Answer Nugget discovery.疫情问答：答案片段发现的问题生成和蕴涵。
J Am Med Inform Assoc. 2023 Jan 18;30(2):329-339. doi: 10.1093/jamia/ocac222.

引用本文的文献

1
Evaluating acute image ordering for real-world patient cases via language model alignment with radiological guidelines.通过与放射学指南的语言模型对齐来评估真实世界患者病例的急性影像检查单开具情况。
Commun Med (Lond). 2025 Aug 4;5(1):332. doi: 10.1038/s43856-025-01061-9.
2
A Dataset of Medical Questions Paired with Automatically Generated Answers and Evidence-supported References.一个包含医学问题以及自动生成答案和有证据支持的参考文献的数据集。
Sci Data. 2025 Jun 19;12(1):1035. doi: 10.1038/s41597-025-05233-z.
3
LabQAR: A Manually Curated Dataset for Question Answering on Laboratory Test Reference Ranges and Interpretation.

本文引用的文献

1
UniProt: the Universal Protein Knowledgebase in 2023.UniProt：2023 年的通用蛋白质知识库。
Nucleic Acids Res. 2023 Jan 6;51(D1):D523-D531. doi: 10.1093/nar/gkac1052.
2
LitCovid: an open database of COVID-19 literature.LitCovid：一个 COVID-19 文献的开放数据库。
Nucleic Acids Res. 2021 Jan 8;49(D1):D1534-D1540. doi: 10.1093/nar/gkaa952.
3
Human Disease Ontology 2018 update: classification, content and workflow expansion.人类疾病本体论 2018 更新：分类、内容和工作流程扩展。
LabQAR：一个人工整理的关于实验室检查参考范围及解读问答的数据集。
medRxiv. 2025 Jun 3:2025.06.03.25328882. doi: 10.1101/2025.06.03.25328882.
4
Unveiling the power of language models in chemical research question answering.揭示语言模型在化学研究问题解答中的力量。
Commun Chem. 2025 Jan 5;8(1):4. doi: 10.1038/s42004-024-01394-x.
5
VAIV bio-discovery service using transformer model and retrieval augmented generation.基于 Transformer 模型和检索增强生成的 VAIV 生物发现服务。
BMC Bioinformatics. 2024 Aug 21;25(1):273. doi: 10.1186/s12859-024-05903-6.
6
Opportunities and challenges for ChatGPT and large language models in biomedicine and health.ChatGPT 和大型语言模型在生物医学和健康领域的机遇与挑战。
Brief Bioinform. 2023 Nov 22;25(1). doi: 10.1093/bib/bbad493.
7
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health.ChatGPT和大语言模型在生物医学与健康领域的机遇与挑战
ArXiv. 2023 Oct 17:arXiv:2306.10070v2.
Nucleic Acids Res. 2019 Jan 8;47(D1):D955-D962. doi: 10.1093/nar/gky1032.
4
The Gene Ontology Resource: 20 years and still GOing strong.《基因本体论资源：20 年，持续强大》
Nucleic Acids Res. 2019 Jan 8;47(D1):D330-D338. doi: 10.1093/nar/gky1055.
5
A dictionary to identify small molecules and drugs in free text.用于识别自由文本中小分子和药物的词典。
Bioinformatics. 2009 Nov 15;25(22):2983-91. doi: 10.1093/bioinformatics/btp535. Epub 2009 Sep 16.
6
GoPubMed: exploring PubMed with the Gene Ontology.GoPubMed：利用基因本体论探索PubMed
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W783-6. doi: 10.1093/nar/gki470.
7
Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.基因本体论：生物学统一工具。基因本体论联合会。
Nat Genet. 2000 May;25(1):25-9. doi: 10.1038/75556.