Behavior Research Methods, Instruments, & Computers

, Volume 28, Issue 2, pp 203–208

Producing high-dimensional semantic spaces from lexical co-occurrence

Analysis Of Semantic And Clinical Data

DOI: 10.3758/BF03204766

Cite this article as:
Lund, K. & Burgess, C. Behavior Research Methods, Instruments, & Computers (1996) 28: 203. doi:10.3758/BF03204766

Abstract

A procedure that processes a corpus of text and produces numeric vectors containing information about its meanings for each word is presented. This procedure is applied to a large corpus of natural language text taken from Usenet, and the resulting vectors are examined to determine what information is contained within them. These vectors provide the coordinates in a high-dimensional space in which word relationships can be analyzed. Analyses of both vector similarity and multidimensional scaling demonstrate that there is significant semantic information carried in the vectors. A comparison of vector similarity with human reaction times in a single-word priming experiment is presented. These vectors provide the basis for a representational model of semantic memory, hyperspace analogue to language (HAL).

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Copyright information

© Psychonomic Society, Inc. 1996

Authors and Affiliations

  1. 1.Psychology DepartmentUniversity of CaliforniaRiverside