Computers understand very little of the meaning of human language. This profoundly\u00a0limits our ability to give instructions to computers, the ability of computers to explain\u00a0their actions to us, and the ability of computers to analyse and process text. Vector space\u00a0models (VSMs) of semantics are beginning to address these limits. This paper surveys the\u00a0use of VSMs for semantic processing of text. We organize the literature on VSMs according\u00a0to the structure of the matrix in a VSM. There are currently three broad classes of VSMs,\u00a0based on term\u2013document, word\u2013context, and pair\u2013pattern matrices, yielding three classes\u00a0of applications. We survey a broad range of applications in these three categories and we\u00a0take a detailed look at a specific open source project in each category. Our goal in this\u00a0survey is to show the breadth of applications of VSMs for semantics, to provide a new\u00a0perspective on VSMs for those who are already familiar with the area, and to provide
\npointers into the literature for those who are less familiar with the field.<\/p>\n","protected":false},"excerpt":{"rendered":"
Computers understand very little of the meaning of human language. This profoundly\u00a0limits our ability to give instructions to computers, the ability of computers to explain\u00a0their actions to us, and the ability of computers to analyse and process text. Vector space\u00a0models (VSMs) of semantics are beginning to address these limits. 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