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Producing high-dimensional semantic spaces from lexical co-occurrence

  • Kevin Lund
  • Curt Burgess
Analysis Of Semantic And Clinical Data

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).

Keywords

Target Word Word Pair Semantic Space Semantic Distance Vector Similarity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Psychonomic Society, Inc. 1996

Authors and Affiliations

  1. 1.Psychology DepartmentUniversity of CaliforniaRiverside

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