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一项关于多语言攻击性语言检测的调查

Khouloud Mnassri¹, Reza Farahbakhsh¹, Razieh Chalehchaleh¹

¹Samovar, Telecom SudParis, Institut Polytechnique de Paris, Palaiseau, France.

PeerJ. Computer science

|April 25, 2024

概括

由于语言的多样性,在多种语言中检测攻击性语言是具有挑战性的. 本调查回顾了用于多语言攻击性语言检测的当前方法和数据集,并提出了对更安全的在线通信的未来研究方向.

科学领域:

自然语言处理自然语言处理.
计算语言学计算语言学
社交媒体分析社交媒体分析

背景情况:

在线攻击性内容越来越普遍,需要有效的检测方法.
多语言攻击性语言由于语言和文化多样性而带来了独特的挑战.
现有的检测系统经常与各种语言和文化背景的细微差别作斗争.

研究的目的:

为了调查当前的多语言攻击性语言检测环境.
在这个领域分析现有的方法,数据集和社区挑战.
确定改进检测系统的未来研究方向.

主要方法:

关于多语言攻击性语言检测研究的综合文献综述.
分析现有数据集及其适用于跨语言任务的适用性.
技术,文化和语言挑战的识别和分类.
探索社区驱动的努力和共同任务.

主要成果:

概述各种多语言攻击性语言检测技术及其性能.
对当前数据集中的局限性和偏见进行批判性评估.
确定阻碍有效跨语言检测的关键挑战.
一个精心策划的资源和数据集列表,用于该领域的研究人员.

结论:

多语言攻击性语言的检测需要解决复杂的语言和文化因素.
需要进一步的研究来开发强大和公平的检测模型.
制定标准化的基准和多样化的数据集对于进步至关重要.
目标是通过先进的检测技术,在全球范围内促进更安全的数字通信.

关键词:

仇恨言论就是一种仇恨言论.文献综述文献综述文献综述多语言主义多语言主义.有攻击性的语言.社交媒体社交媒体

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