命名实体识别方法及在电力领域的应用

Methods of named entity recognition and applications in electric power domain

摘要

领域命名实体识别方法在大语言模型技术赋能的背景下,推动了电力领域命名实体识别的发展。在此背景下,本文综述电力领域命名实体识别方法的发展历程,介绍早期基于规则和词典的方法再到统计机器学习的方法。从分布式嵌入层、文本编码层和标签解码层总结基于深度学习方法的模型。本文还探讨大语言模型在命名实体识别任务中的应用及其影响,并且探索当前电力领域命名实体识别存在的问题。

Abstract

The development of named entity recognition (NER) in the power sector has been propelled by domain-specific NER methods under the empowerment of large language model technology. In this context, the evolutionary journey of NER methods within the power domain is reviewed in this paper, where early approaches based on rules and dictionaries are introduced, followed by statistical machine learning methods. Deep learning-based models are summarized from the perspectives of the distributed embedding layer, the text encoding layer, and the label decoding layer. The application of large language models to NER tasks and their impact are also examined. Furthermore, the existing challenges currently faced by power domain NER are explored. Finally, an outlook on future research directions is presented.

关键词

领域命名实体识别 / 电力领域 / 大语言模型 / 机器学习 / 深度学习

Key words

domain named entity recognition / power domain / large language model / machine learning / deep learning

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基金资助

广东省重点实验室基金资助项目(2023B1212060076)

深圳市科技计划基金资助项目(KJZD20230923114405012)

南方电网科技基金资助项目(031900KC23040016(GDKJXM20230399))

南方电网科技基金资助项目(031900KC23040017(GDKJXM20230401))