This paper presents a new method for automatically detecting words with
lexical gender in large-scale language datasets. Currently, the evaluation of
gender bias in natural language processing relies on manually compiled lexicons
of gendered expressions, such as pronouns ('he', 'she', etc.) and nouns with
lexical gender ('mother', 'boyfriend', 'policewoman', etc.). However, manual
compilation of such lists can lead to static information if they are not
periodically updated and often involve value judgments by individual annotators
and researchers. Moreover, terms not included in the list fall out of the range
of analysis. To address these issues, we devised a scalable, dictionary-based
method to automatically detect lexical gender that can provide a dynamic,
up-to-date analysis with high coverage. Our approach reaches over 80% accuracy
in determining the lexical gender of nouns retrieved randomly from a Wikipedia
sample and when testing on a list of gendered words used in previous research.

本文介绍了一种自动检测大型语言数据集中具有词汇性别的单词的新方法，以解决手动编译性别化表达的词汇列表的静态问题和无法在分析范围之内的单词问题。该方法是基于字典的，可提供动态的、更新至最新的高覆盖分析，并在随机可获取的维基百科示例中达到了 80％的准确率，在以前的研究中使用的性别化词汇列表测试时也表现良好。