Data-driven analyses of biases in historical texts can help illuminate the
origin and development of biases prevailing in modern society.
However, digitised historical documents pose a challenge for NLP
practitioners as these corpora suffer from errors introduced by optical
character recognition (OCR) and are written in an archaic language. In this
paper, we investigate the continuities and transformations of bias in
historical newspapers published in the Caribbean during the colonial era (18th
to 19th centuries). Our analyses are performed along the axes of gender, race,
and their intersection. We examine these biases by conducting a temporal study
in which we measure the development of lexical associations using
distributional semantics models and word embeddings. Further, we evaluate the
effectiveness of techniques designed to process OCR-generated data and assess
their stability when trained on and applied to the noisy historical newspapers.
We find that there is a trade-off between the stability of the word embeddings
and their compatibility with the historical dataset. We provide evidence that
gender and racial biases are interdependent, and their intersection triggers
distinct effects. These findings align with the theory of intersectionality,
which stresses that biases affecting people with multiple marginalised
identities compound to more than the sum of their constituents.

本文通过时间序列的词汇联想分析，以及针对 OCR 错误引入噪音的技术处理，研究了加勒比地区殖民时期（18 世纪到 19 世纪）历史报纸中性别、种族偏见的连续性和转化。研究结果表明，种族和性别偏见是相互依存的，两者的交叉触发了不同效应，这与交叉性理论一致。

测量历史文献中的交叉偏见

Measuring Intersectional Biases in Historical Documents

Distributional semantics models are known to struggle with small data. It is
generally accepted that in order to learn 'a good vector' for a word, a model
must have sufficient examples of its usage. This contradicts the fact that
humans can guess the meaning of a word from a few occurrences only. In this
paper, we show that a neural language model such as Word2Vec only necessitates
minor modifications to its standard architecture to learn new terms from tiny
data, using background knowledge from a previously learnt semantic space. We
test our model on word definitions and on a nonce task involving 2-6 sentences'
worth of context, showing a large increase in performance over state-of-the-art
models on the definitional task.

本论文研究了分布式语义模型与小样本数据的问题，并展示了如何使用神经语言模型 Word2Vec，通过以前学习语义空间的背景知识，仅对其标准体系结构进行微小修改，以从微小数据中学习新术语，并在单词定义任务和少量上下文的例子任务中，相较于最先进的模型，呈现出大幅增长的性能。