Computer Science – Computation and Language
Scientific paper
2010-03-04
Journal of Artificial Intelligence Research, (2010), 37, 141-188
Computer Science
Computation and Language
Scientific paper
10.1613/jair.2934
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.
Pantel Patrick
Turney Peter D.
No associations
LandOfFree
From Frequency to Meaning: Vector Space Models of Semantics does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with From Frequency to Meaning: Vector Space Models of Semantics, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and From Frequency to Meaning: Vector Space Models of Semantics will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-686702