Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.

该论文总结并分类了向量空间模型在语义文本处理中的应用，分为三类主要矩阵结构：项-文档、词-上下文和对模式矩阵，主要介绍了这三个类别中的广泛应用及其在开源项目中的详细研究，并旨在为熟悉或不熟悉该领域的人提供一些新的研究视角。

从频率到意义：向量空间语义模型