CHIP2021-医疗对话临床发现阴阳性判别数据集

数据集来源于CHIP2021医疗对话临床发现阴阳性判别学术评测任务：http://cips-chip.org.cn/2021/eval1 , 数据集包括6,000段训练数据，4,000段测试数据。 本数据集由阿里夸克医疗事业部提供。<br/>
<font color=red>此数据集在CBLUE（https://tianchi.aliyun.com/cblue）评测基准开放了长期的leaderboard，对应的数据集名为CHIP-MDCFNPC，且CBLUE榜单的CHIP-MDCFNPC对标注质量做了修复，请申请CBLUE数据集，同时也欢迎在CBLUE榜单上继续提交评估模型。</font> <br/><br/>

夸克在CHIP2022会议继续举办了评测任务 - 医学因果实体关系抽取任务：https://tianchi.aliyun.com/dataset/dataDetail?dataId=129573 ，欢迎报名参加。

This dataset is derived from the academic evaluation task of Clinical Finding Polarity Classification in Medical Dialogues held at CHIP2021, with the official webpage: http://cips-chip.org.cn/2021/eval1. It contains 6,000 training samples and 4,000 test samples, and is provided by the Quark Medical Division of Alibaba Group.
This dataset has a long-term leaderboard on the CBLUE (https://tianchi.aliyun.com/cblue) evaluation benchmark under the dataset name CHIP-MDCFNPC. The annotation quality of CHIP-MDCFNPC on the CBLUE leaderboard has been revised. Please apply for the CBLUE dataset, and submissions for model evaluation on the CBLUE leaderboard are also welcome.

Quark also hosted the evaluation task - Medical Causal Entity Relation Extraction, at the CHIP2022 conference, with the official link: https://tianchi.aliyun.com/dataset/dataDetail?dataId=129573. Participants are welcome to register for the task.

搜集汇总

背景与挑战

背景概述

CHIP2021-医疗对话临床发现阴阳性判别数据集是一个用于医疗对话中临床发现阴阳性分类的数据集，包含10,000段标注数据，任务目标是预测临床发现的阴阳性状态。