面向仇恨言论检测的群体特异性自然语言处理方法
A Group-Specific Approach to NLP for Hate Speech Detection
AI总结：
本文提出面向特定受保护群体的仇恨言论检测NLP方法，并以反犹言论检测为例，构建历史语言知识图谱、注入模型并结合伦理与交叉性评估，证明其有助于识别细微刻板印象。
摘要：
自动仇恨言论检测是一项重要而复杂的任务，需要常识、关于受保护群体的刻板印象以及歧视历史等知识，而这些知识都可能不断演变。本文提出一种用于在线仇恨言论检测的群体特异性自然语言处理方法。该方法包括：创建并将关于某一特定受保护群体的历史和语言知识注入仇恨言论检测模型；分析针对某一受保护群体的歧视历史数据，以更好地预测针对该群体的仇恨言论激增；并从交叉性和伦理视角批判性地评估仇恨言论检测模型。我们通过一个关于检测反犹仇恨言论的自然语言处理案例研究展示该方法。该案例研究综合了当前关于反犹主义检测的英文自然语言处理文献，引入了一个涵盖20世纪至今反犹历史与语言的新型知识图谱，将该知识图谱中的信息注入一组推文，并基于Logistic Regression和uncased DistilBERT基线进行实验，结果表明纳入知识图谱中的上下文可以帮助模型捕捉细微的刻板印象。
英文摘要：
Automatic hate speech detection is an important yet complex task, requiring knowledge of common sense, stereotypes of protected groups, and histories of discrimination, each of which may constantly evolve. In this paper, we propose a group-specific approach to NLP for online hate speech detection. The approach consists of creating and infusing historical and linguistic knowledge about a particular protected group into hate speech detection models, analyzing historical data about discrimination against a protected group to better predict spikes in hate speech against that group, and critically evaluating hate speech detection models through lenses of intersectionality and ethics. We demonstrate this approach through a case study on NLP for detection of antisemitic hate speech. The case study synthesizes the current English-language literature on NLP for antisemitism detection, introduces a novel knowledge graph of antisemitic history and language from the 20th century to the present, infuses information from the knowledge graph into a set of tweets over Logistic Regression and uncased DistilBERT baselines, and suggests that incorporating context from the knowledge graph can help models pick up subtle stereotypes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
