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相关概念视频

Language and Cognition01:27

Language and Cognition

352
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
352
Language Development01:22

Language Development

369
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
369

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相关实验视频

Updated: Jul 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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基于语言推断的学习,用于低资源的中文临床命名实体识别,使用语言模型.

Zhaojian Cui1, Kai Yu1, Zhenming Yuan1

  • 1Schoolof Information Science and Technology, Hangzhou Normal University, Hangzhou, 311121 China.

Journal of biomedical informatics
|December 6, 2023
PubMed
概括

这项研究介绍了LANGIL,这是一种在低资源场景中用于临床命名实体识别 (CNER) 的新方法. 兰吉尔有效地从电子健康记录 (EHR) 中提取信息,使用有限的注释数据,改进现有方法.

关键词:
临床命名实体的认可.电子健康记录电子健康记录语言推断的推断语言推断.低资源的低资源的人.预先训练有素的语言模型

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科学领域:

  • 自然语言处理自然语言处理.
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 电子健康记录 (EHR) 越来越多地取代纸质记录,需要有效的信息提取.
  • 临床命名实体识别 (CNER) 对于从EHR中提取有价值的数据至关重要.
  • 传统的CNER监督方法需要大量的注释数据,这在临床环境中很少.

研究的目的:

  • 解决临床命名实体识别 (NER) 中有限的注释数据的挑战.
  • 为低资源的临床NER提出一种新的基于推断的语言学习方法 (LANGIL).
  • 用最少的训练样本利用预训练语言模型 (PLM) 的理解能力.

主要方法:

  • 开发了一种基于语言推断的学习方法 (LANGIL) 用于临床NER.
  • 将实体识别任务重构为使用快速学习的语言推断问题.
  • 避免从头开始培训额外的网络层,减轻培训前/下游任务差距.

主要成果:

  • 在四个中国临床NER数据集上,LANGIL在F1得分上显著改善.
  • 拟议的方法有效地利用PLM在低资源条件下的理解能力.
  • 与以前的方法相比,在稀缺数据场景中实现了更好的性能.

结论:

  • 兰吉尔为低资源环境中的临床NER提供了一个有前途的解决方案.
  • 基于语言推断的方法增强了从有限注释的电子健康记录中提取信息.
  • 这种方法减轻了临床自然语言处理研究中的数据注释瓶.