用自然语言处理量化前现代英语文学中的性别差异

M. Kejriwal, Akarsh Nagaraj
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摘要

研究继续阐明了社会、文化和经济领域的性别差异的程度和意义。最近,来自数据科学和自然语言处理(NLP)社区的计算工具被提议使用经验严格的方法来大规模测量这种差异。在这篇文章中,我们通过研究前现代时期出版的2443篇版权过期的文学文本中的性别差异,为这条研究线做出了贡献。在这项工作中,前现代时期被定义为从19世纪初到20世纪初的时期。使用可复制的数据科学方法,依赖于公开可用和已建立的NLP组件,我们在这些文本中提取了三种不同的性别字符流行度量。我们使用了一套广泛的统计测试来有力地证明了前现代文学中女性角色和男性角色的流行程度之间存在显著差异。我们还发现,在女性创作的文学文本中,女性角色的比例显著高于男性创作的文学文本。然而,基于回归的分析表明,在语料库覆盖的120年期间,女性角色的流行率并没有随着时间的推移而显著变化,无论作者的性别如何,女性角色的流行率仍然低于50%的平价水平。定性分析进一步表明,语料库中与女性角色相关的描述与与男性角色相关的描述明显不同(和刻板)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying Gender Disparity in Pre-Modern English Literature using Natural Language Processing
Research has continued to shed light on the extent and significance of gender disparity in social, cultural and economic spheres. More recently, computational tools from the data science and Natural Language Processing (NLP) communities have been proposed for measuring such disparity at scale using empirically rigorous methodologies. In this article, we contribute to this line of research by studying gender disparity in 2,443 copyright-expired literary texts published in the pre-modern period, defined in this work as the period ranging from the beginning of the nineteenth through the early twentieth century. Using a replicable data science methodology relying on publicly available and established NLP components, we extract three different gendered character prevalence measures within these texts. We use an extensive set of statistical tests to robustly demonstrate a significant disparity between the prevalence of female characters and male characters in pre-modern literature. We also show that the proportion of female characters in literary texts significantly increases in female-authored texts compared to the same proportion in male-authored texts. However, regression-based analysis shows that, over the 120 year period covered by the corpus, female character prevalence does not change significantly over time, and remains below the parity level of 50%, regardless of the gender of the author. Qualitative analyses further show that descriptions associated with female characters across the corpus are markedly different (and stereotypical) from the descriptions associated with male characters.
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