论文标题
通过多维缩放生成语义图：语言应用和理论
Generating semantic maps through multidimensional scaling: linguistic applications and theory
论文作者
论文摘要
本文报告了在应用多维缩放（MDS）技术的最新技术中，以创建语言研究中的语义图。 MDS是指代表对象（词汇项目，语言上下文，语言等）的统计技术作为空间中的点，从而使对象之间的密切相似性对应于表示表示相应点之间的紧密距离。我们专注于使用MDS与跨语言变化研究中使用的平行语料库数据结合使用。 我们首先介绍了MD的数学基础，然后对过去的研究进行了详尽的概述，该研究采用了MDS技术与并行语料库数据结合使用。我们建议一组术语，以简洁地描述特定MDS应用程序的关键参数。然后，我们证明这种计算方法是理论中性的，即可以用来回答各种语言理论框架的研究问题。最后，我们展示了这如何导致语言学中MDS研究的未来发展。
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.
