Abstract

Recently, the use of word embedding models (WEM) has received ample attention in the natural language processing community. These models can capture semantic information in large corpora of text by learning distributional properties of words, that is how often particular words appear in specific contexts. Scholars have pointed out the potential of WEMs for historical research. In particular, their ability to capture semantic change might assist historians studying conceptual change or specific discursive formations over time. Concurrently, others voiced their criticism and pointed out that WEMs require large amounts of training data, that they are challenging to evaluate, and they lack the specificity looked for by historians. The ability to examine semantic change resonates with the goals of historians such as Reinhart Koselleck, whose research focused on the formation of concepts and the transformation of semantic fields. However, word embeddings can only be used to study particular types of semantic change, and the model’s use is dependent on the size, quality, and bias in training data. In this article, we examine what is required of historical data to produce reliable WEMs, and we describe the types of questions that can be answered using WEMs.

摘要

最近，单词嵌入模型（WEM）的使用在自然语言处理社区中受到了广泛关注。这些模型可以通过学习单词的分布特性（即特定单词在特定上下文中出现的频率）来捕获大文本集的语义信息。学者们指出了WEM在历史研究中的潜力。特别是，他们掌握语义变化的能力可能会帮助历史学家研究概念变化或特定话语形式随时间的推移。同时，其他人对此表示了批评，并指出WEM需要大量的训练数据，它们难以评估，并且缺乏历史学家寻找的特殊性。检查语义变化的能力与Reinhart Koselleck等历史学家的目标产生共鸣，他的研究集中于概念的形成和语义领域的转变。但是，单词嵌入只能用于研究特定类型的语义变化，并且模型的使用取决于训练数据的大小，质量和偏差。在本文中，我们研究了生成可靠的WEM所需的历史数据，并描述了可以使用WEM回答的问题类型。