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深层知识处理与语义计算/Entity-Linking

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README
MulanPSL-2.0

实体链指系统DuEL

概要

DuEL实体链接系统对应模型是一个多任务模型,包含两个任务:候选实体排名和提及类型的预测。

DuEL可作为CCKS 2020实体链指基线系。 CCK2020在CCKS 2019面向中文短文本的实体链指任务的基础上进行了拓展与改进。有关数据集和任务的详细信息,请访问比赛的官方网站。

实现描述

候选实体排序任务使用pairwise模型。在训练阶段,将query和候选实体描述分别输入Ernie网络以query表示和候选实体表示。 之后,合并query表示和候选实体表示并经过MLP网络进行打分。这里使用的损失函数是rank_loss。

Mention 类型预测任务使用分类模型。在训练阶段,将query输入到Ernie网络中以获取表示。 之后,query表示向量通过MLP获得mention类型。这里的损失函数是分类损失。

最后,我们将两个损失合并后,进行多任务训练。

在预测阶段,我们使用预测的mention类型来验证候选实体。

注,在训练和预测阶段,我们将NIL视为实体ID,详细信息见代码。

我们将一些模型输入数据的示例放在dir ./data/generated/下,该数据可以通过代码./data/data_process.py生成。

环境

Python2 + Paddle Fluid 1.5 (请在脚本中确认您的Python路径)。

需求包在./requirements.txt中列出

代码在单个P40 GPU上进行了测试,CUDA版本=10.0

下载数据

请从竞赛网站上下载数据,解压后放在./data/basic_data/目录

解压后,目录./data/basic_data/ 中包含文件:

dev.json
kb.json
test.json
train.json
eval.py
README
CCKS 2020 Entity Linking License.docx

下载预训练的Ernie模型

下载ERNIE1.0 Base模型,并将其解压到./pretrained_model/

cd ./pretrained_mdoel/

wget --no-check-certificate https://ernie.bj.bcebos.com/ERNIE_1.0_max-len-512.tar.gz

tar -zxvf ERNIE_1.0_max-len-512.tar.gz

解压后,路径./pretrained_model/ERNIE_1.0_max-len-512 中包含文件:

ernie_config.json
params
vocab.txt

数据格式转换

生成的数据在./data/generated/目录中

cd ./data/

python data_process.py

训练

sh ./script/train.sh

默认情况在,模型将保存到./checkpoints/

训练过程中会打印准确率和f1

训练和预测前注意调整python路径和数据集路径

建议使用10000条以下的数据作为验证集,以免验证集过大导致整体耗时增加

预测

调整预测脚本中的模型路径,运行:

sh ./script/predict.sh

预测结果将以与原始数据集的相同的格式写入json文件(与最终官方评估格式相同)。预测结果路径为./data/generated/test_pred.json

评估

修改评估文件中的预测结果路径(使用dev数据集进行本地评估,需调整预测步骤的数据集和数据生成过程的is_train参数),运行:

python ./data/eval.py

提交生成的test_pred.json到竞赛平台进行测试集评估。

Copyright and License

Copyright 2020 Baidu.com, Inc. All Rights Reserved

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may otain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

附录

数据集包含24种实体类型,详情见下表

Type 中文名
Event 事件活动
Person 人物
Work 作品
Location 区域场所
Time&Calendar 时间历法
Brand 品牌
Natural&Geography 自然地理
Game 游戏
Biological 生物
Medicine 药物
Food 食物
Software 软件
Vehicle 车辆
Website 网站平台
Disease&Symptom 疾病症状
Organization 组织机构
Awards 奖项
Education 教育
Culture 文化
Constellation 星座
Law&Regulation 法律法规
VirtualThings 虚拟事物
Diagnosis&Treatment 诊断治疗方法
Other 其他

致谢和声明

本开源项目受国家重点研发计划“云计算和大数据”专项支持(项目号 ** 2018YFB1004300 ** )。

木兰宽松许可证, 第2版 木兰宽松许可证, 第2版 2020年1月 http://license.coscl.org.cn/MulanPSL2 您对“软件”的复制、使用、修改及分发受木兰宽松许可证,第2版(“本许可证”)的如下条款的约束: 0. 定义 “软件”是指由“贡献”构成的许可在“本许可证”下的程序和相关文档的集合。 “贡献”是指由任一“贡献者”许可在“本许可证”下的受版权法保护的作品。 “贡献者”是指将受版权法保护的作品许可在“本许可证”下的自然人或“法人实体”。 “法人实体”是指提交贡献的机构及其“关联实体”。 “关联实体”是指,对“本许可证”下的行为方而言,控制、受控制或与其共同受控制的机构,此处的控制是指有受控方或共同受控方至少50%直接或间接的投票权、资金或其他有价证券。 1. 授予版权许可 每个“贡献者”根据“本许可证”授予您永久性的、全球性的、免费的、非独占的、不可撤销的版权许可,您可以复制、使用、修改、分发其“贡献”,不论修改与否。 2. 授予专利许可 每个“贡献者”根据“本许可证”授予您永久性的、全球性的、免费的、非独占的、不可撤销的(根据本条规定撤销除外)专利许可,供您制造、委托制造、使用、许诺销售、销售、进口其“贡献”或以其他方式转移其“贡献”。前述专利许可仅限于“贡献者”现在或将来拥有或控制的其“贡献”本身或其“贡献”与许可“贡献”时的“软件”结合而将必然会侵犯的专利权利要求,不包括对“贡献”的修改或包含“贡献”的其他结合。如果您或您的“关联实体”直接或间接地,就“软件”或其中的“贡献”对任何人发起专利侵权诉讼(包括反诉或交叉诉讼)或其他专利维权行动,指控其侵犯专利权,则“本许可证”授予您对“软件”的专利许可自您提起诉讼或发起维权行动之日终止。 3. 无商标许可 “本许可证”不提供对“贡献者”的商品名称、商标、服务标志或产品名称的商标许可,但您为满足第4条规定的声明义务而必须使用除外。 4. 分发限制 您可以在任何媒介中将“软件”以源程序形式或可执行形式重新分发,不论修改与否,但您必须向接收者提供“本许可证”的副本,并保留“软件”中的版权、商标、专利及免责声明。 5. 免责声明与责任限制 “软件”及其中的“贡献”在提供时不带任何明示或默示的担保。在任何情况下,“贡献者”或版权所有者不对任何人因使用“软件”或其中的“贡献”而引发的任何直接或间接损失承担责任,不论因何种原因导致或者基于何种法律理论,即使其曾被建议有此种损失的可能性。 6. 语言 “本许可证”以中英文双语表述,中英文版本具有同等法律效力。如果中英文版本存在任何冲突不一致,以中文版为准。 条款结束 如何将木兰宽松许可证,第2版,应用到您的软件 如果您希望将木兰宽松许可证,第2版,应用到您的新软件,为了方便接收者查阅,建议您完成如下三步: 1, 请您补充如下声明中的空白,包括软件名、软件的首次发表年份以及您作为版权人的名字; 2, 请您在软件包的一级目录下创建以“LICENSE”为名的文件,将整个许可证文本放入该文件中; 3, 请将如下声明文本放入每个源文件的头部注释中。 Copyright (c) [Year] [name of copyright holder] [Software Name] is licensed under Mulan PSL v2. 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简介

实体链指系统DuEL:面向中文短文本的实体链接任务,是一个多任务模型,包含两个任务:候选实体排名和提及类型的预测。本开源项目受国家重点研发计划“云计算和大数据”专项支持(项目号 **2018YFB1004300 ** )。 展开 收起
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