ZhangbuDong 9a8b829a88 | 1 year ago | |
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README.md | 1 year ago |
在医学搜索中,对搜索问题的意图分类可以极大提升搜索结果的相关性,特别是医学知识具备极强的专业性,对问题意图进行分类也有助于融入医学知识来做增强搜索结果的性能。本任务数据集就是在这样的背景下产生的。
在本评测任务中,给定医学搜索问题,要求对医学问题进行意图分类。医学问题分为病情诊断(diagnosis)、病因分析(cause)、治疗方案(method)、就医建议(advice)、指标解读(metric_explain)、疾病描述(disease_express)、后果表述(result)、注意事项(attention)、功效作用(effect)、医疗费用(price)、其他(other) 共11种类型。
在本次评测中,医学问题分为 病情诊断(diagnosis)、病因分析(cause)、治疗方案(method)、就医建议(advice)、指标解读(metric_explain)、疾病描述(disease_express)、后果表述(result)、注意事项(attention)、功效作用(effect)、医疗费用(price)、其他(other) 共11种类型,类型说明和示例如下:
病情诊断:已知症状,判断可能的原因, 如:
最近早上起来浑身无力是怎么回事?
我家宝宝快五个月了,为什么偶尔会吐清水带?
病因分析:已知疾病,解释疾病发生的原因。如:
阴道松弛的原因是什么?
鼻咽癌是如何发生的?
治疗方案:已知疾病/症状,给出治疗或缓解的方案(检查/手术/药物/行为)。如:
腰椎间盘突出可以烤电吗
感冒头疼吃什么药好
宝宝感冒眼屎多又黄怎么办
烫伤的疤痕要怎么去除?
就医建议:已知症状/疾病,给出就医建议(科室/检查)。如:
糖尿病该做什么检查?
肚子疼去什么科室?
指标解读:身高/体重/血压等检查结果的数值范围解读。如:
血常规超敏C反应蛋白偏高说明什么
b超检查报告写的检测到盆腔积液是11mm,严重么?
疾病描述:疾病属性(eg:能不能治、能不能治好)、症状、表现、图片等相关表述。如:
外痔疮早期症状有哪些呢?
白癜风能不能治愈
后果表述:疾病/症状/药品/检查项/食物的危害,疾病恶化不治疗会产生的不良影响或治疗后会产生好的结果。如:
缺乏钾元素会怎么样
乙肝不治疗会怎么样
注意事项:病人要注意的事情,以及分析食物的好坏,食物对病人的影响。如:
哮喘应该注意些什么
孕妇能不能吃榴莲
柿子不能和什么一起吃
糖尿病人饮食注意什么啊?
功效作用:食品/药物的好处,功效/作用/副作用。如:
乌鸡白凤丸的功效和作用
玫瑰,柠檬,菊花可以一起泡吗?有什么功效
医疗费用:疾病/手术/药品/检查/的费用。如:
二甲双瓜要多少钱?
其他:无法涵盖在前面分类里的以及低价值/无意义/非医疗、需求不明没讲明白的。如:
玻尿酸丰唇能保持多久
血常规五分类是查什么
本评测开放训练集数据6931条,验证集数据1955条,测试集数据1994条。
数据集名称为:KUAKE-QIC(KUAKE - Query Intent Criterion dataset)。
数据集下载文件为:KUAKE-QIC.zip, 包括:
KUAKE-QIC_train.json: 训练集
KUAKE-QIC_dev.json: 验证集
KUAKE-QIC_test.json: 测试集,选手提交的时候需要为每条记录增加“label”字段
example_gold.json: 标准答案示例
example_pred.json: 提交结果示例
README.txt: 说明文件
数据集提供方
阿里夸克
数据集负责人
尹康平 阿里夸克
[
{
"id": "s1",
"query": "心肌缺血如何治疗与调养呢?",
"label": "治疗方案"
},
{
"id": "s2",
"query": "19号来的月经,25号服用了紧急避孕药本月5号,怎么办?",
"label": "治疗方案"
},
{
"id": "s3",
"query": "什么叫痔核脱出?什么叫外痔?",
"label": "疾病表述"
}
]
@inproceedings{zhang-etal-2022-cblue,
title = "{CBLUE}: A {C}hinese Biomedical Language Understanding Evaluation Benchmark",
author = "Zhang, Ningyu and
Chen, Mosha and
Bi, Zhen and
Liang, Xiaozhuan and
Li, Lei and
Shang, Xin and
Yin, Kangping and
Tan, Chuanqi and
Xu, Jian and
Huang, Fei and
Si, Luo and
Ni, Yuan and
Xie, Guotong and
Sui, Zhifang and
Chang, Baobao and
Zong, Hui and
Yuan, Zheng and
Li, Linfeng and
Yan, Jun and
Zan, Hongying and
Zhang, Kunli and
Tang, Buzhou and
Chen, Qingcai",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.544",
pages = "7888--7915",
abstract = "Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually offering great promise for medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.",
}
在医学搜索中,对搜索问题的意图分类可以极大提升搜索结果的相关性,特别是医学知识具备极强的专业性,对问题意图进行分类也有助于融入医学知识来做增强搜索结果的性能。本任务数据集就是在这样的背景下产生的。
other
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