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个人第一和身份第一语言:文本挖掘探讨遗传学家如何讨论自闭症

J Kasmire^1,2, Andrada Ciucă³, Ramona Moldovan^4,5

¹UK Data Service, Cathie Marsh Institute, University of Manchester, Manchester, UK.

Health informatics journal

|February 17, 2025

概括

在自闭症遗传学研究中,人第一语言 (PFL) 和身份第一语言 (IFL) 都被类似地使用. 自然语言处理 (NLP) 量化了它们的使用,有助于为科学出版物制定未来的语言准则.

科学领域:

遗传学是一个遗传学.
语言学的语言学.
医疗信息学医疗信息学

背景情况:

关于自闭症谱系障碍 (ASD) 适当术语的讨论通常涉及人第一语言 (PFL) 和身份第一语言 (IFL).
遗传学研究中的现有语言指南涵盖了残疾和种族,但在ASD的背景下缺乏针对PFL和IFL的具体建议.
在科学传播中,PFL与IFL的最佳使用仍然是激烈辩论的领域.

研究的目的:

应用自然语言处理 (NLP) 方法来分析PFL和IFL在遗传学研究摘要中的使用模式.
量化PFL和IFL在已发表的遗传学研究中的患病率,随时间的变化趋势和上下文使用情况.
探索PFL和IFL在同一研究文章中的同时出现.

主要方法:

使用自然语言处理 (NLP) 来分析欧洲人类遗传学学会 (ESHG) 会议 (2001-2021) 的大约5000个摘要,其中包含与自闭症谱系障碍 (ASD) 相关的关键词.
NLP分析的重点是识别和量化个人第一语言 (PFL) 和身份第一语言 (IFL) 的实例.
语言使用模式随着时间的推移而被检查,与特定名词结合,并在摘要中相互结合.

主要成果:

在整个研究期间,观察到PFL (262例) 和IFL (264例) 的使用情况相似且一致.
大多数PFL和IFL病例是直接匹配的,例如"患有ASD的患者"或"ASD患者".
在经常与PFL和IFL同时出现的名词中发现了微妙的差异;50个摘要使用了PFL和IFL模式.

结论:

自然语言处理 (NLP) 提供了一种可靠的方法来量化PFL和IFL在科学研究中的使用,时间和背景.
这些发现表明NLP在分析科学文献中的语言趋势方面具有实用性.
NLP可以有效地支持基于证据的语言风格准则的开发,并帮助评估这些准则在遗传学研究中的影响,特别是在自闭症谱系障碍 (ASD) 中.

关键词:

自闭症自闭症是什么人类遗传学人类遗传学语言的变化语言的变化自然语言处理自然语言处理.文本挖掘是一种文本挖掘.

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