CCL23 古籍命名实体识别评测——开放赛道

本次古籍文献的命名实体识别评测，通过发布全新的基于“二十四史”的训练和测试数据集，提供统一的评测提交平台，以此推动技术的突破和发展，助力古籍资源的智能开发与利用。
<br />
官方网址：<a href="https://guner2023.pkudh.org/" target="_blank">https://guner2023.pkudh.org/</a>
<br /><br />
请注意，此页面是开放赛道的评测提交入口，要求参赛队伍使用 ChatGPT、文心一言、ChatGLM 等大模型。
封闭赛道的评测提交入口为：<a href="https://tianchi.aliyun.com/dataset/151499" target="_blank">https://tianchi.aliyun.com/dataset/151499</a>。要求参赛队伍禁止使用大模型。仅允许使用拥有开源License（如 GPL、BSD、MIT、Apache 等）且参数量小于 10B 的预训练模型。
<br /><br />
请注意：评测榜单提交时，“组织”一栏请务必填写报名时的队伍名。若之前提交时填写有误，可联系我们撤回。

This named entity recognition (NER) evaluation for ancient Chinese literature releases a brand-new training and test dataset based on the Twenty-Four Histories, and provides a unified evaluation submission platform to promote technological breakthroughs and development, and facilitate the intelligent development and utilization of ancient Chinese literature resources.
<br />
Official website: <a href="https://guner2023.pkudh.org/" target="_blank">https://guner2023.pkudh.org/</a>
<br /><br />
Please note that this page is the evaluation submission portal for the Open Track, where participating teams are required to use large language models (LLMs) such as ChatGPT, Wenxin Yiyan, and ChatGLM.
<br /><br />
The evaluation submission portal for the Closed Track is: <a href="https://tianchi.aliyun.com/dataset/151499" target="_blank">https://tianchi.aliyun.com/dataset/151499</a>. Participating teams are prohibited from using large language models. Only pretrained models with open-source licenses (such as GPL, BSD, MIT, Apache, etc.) and fewer than 10 billion parameters are permitted.
<br /><br />
Please note: When submitting results to the evaluation leaderboard, please be sure to fill in the team name used during registration in the "Organization" field. If the information filled in during previous submissions is incorrect, you may contact us to withdraw the submission.

搜集汇总

背景与挑战

背景概述

该数据集是CCL23古籍命名实体识别评测的开放赛道数据集，基于“二十四史”构建，包含15.4万字的训练和测试数据，标注了人名、书名和官职名三种实体类型。开放赛道要求参赛者使用大模型（如ChatGPT、文心一言等）进行实体识别，旨在推动古籍文本处理技术的突破，促进古籍资源的智能化利用。