Abstract This paper reports on the state-of-the-art in application of multidimensional scaling (MDS) techniques to create semantic maps in linguistic research. MDS refers to a statistical technique that represents objects (lexical items, linguistic contexts, languages, etc.) as points in a space so that close similarity between the objects corresponds to close distances between the corresponding points in the representation. We focus on the use of MDS in combination with parallel corpus data as used in research on cross-linguistic variation. We first introduce the mathematical foundations of MDS and then give an exhaustive overview of past research that employs MDS techniques in combination with parallel corpus data. We propose a set of terminology to succinctly describe the key parameters of a particular MDS application. We then show that this computational methodology is theory-neutral, i.e. it can be employed to answer research questions in a variety of linguistic theoretical frameworks. Finally, we show how this leads to two lines of future developments for MDS research in linguistics.

中文翻译：

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通过多维缩放生成语义图：语言应用和理论

摘要 本文报告了应用多维缩放 (MDS) 技术在语言研究中创建语义图的最新技术。MDS 指的是一种统计技术，它将对象（词汇、语言上下文、语言等）表示为空间中的点，以便对象之间的紧密相似性对应于表示中对应点之间的紧密距离。我们专注于将 MDS 与用于跨语言变异研究的并行语料库数据结合使用。我们首先介绍了 MDS 的数学基础，然后对过去将 MDS 技术与并行语料库数据相结合的研究进行了详尽的概述。我们提出了一组术语来简洁地描述特定 MDS 应用程序的关键参数。然后，我们表明这种计算方法是理论中立的，即它可以用来回答各种语言理论框架中的研究问题。最后，我们展示了这如何导致语言学中 MDS 研究的两条未来发展方向。