计算机科学
关系抽取
管道(软件)
命名实体识别
关系(数据库)
人工智能
接头(建筑物)
信息抽取
任务(项目管理)
深度学习
嵌入
机器学习
校长(计算机安全)
情报检索
自然语言处理
数据挖掘
工程类
建筑工程
系统工程
程序设计语言
操作系统
作者
Mina Esmail Zadeh Nojoo Kambar,Armin Esmaeilzadeh,Maryam Heidari
标识
DOI:10.1109/aiiot54504.2022.9817231
摘要
Named Entity Recognition (NER) and Relation Extraction (RE) are two principal subtasks of knowledge-based systems that extract meaningful information from unstructured text. With Recent advances in Deep Learning techniques, new models use Joint Named Entities and Relation Extraction (JNERE) techniques that simultaneously accomplish NER and RE subtasks. These models avoid the drawbacks of using the traditional pipeline method. As contributions of our study to the other related works, we specifically survey JNERE techniques. The reason for not focusing on pipeline methods or other older techniques is the recent advances of JNERE methods in achieving the state-of-art results for most databases. Additionally, we provide a comprehensive report on the embedding techniques and datasets available for this task. Finally, we discuss the approaches and how they imnpoved the results.
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