title: Incremental Construction of an Associative Network from a Corpus
creator: Lemaire, Benoît
creator: Denhière, Guy
subject: Statistical Models
subject: Machine Learning
subject: Psycholinguistics
description: This paper presents a computational model of the incremental construction of an associative network from a corpus. It is aimed at modeling the development of the human semantic memory. It is not based on a vector representation, which does not well reproduce the asymmetrical property of word similarity, but rather on a network representation. Compared to Latent Semantic Analysis, it is incremental which is cognitively more plausible. It is also an attempt to take into account higher-order co-occurrences in the construction of word similarities. This model was compared to children association norms. A good correlation as well as a similar gradient of similarity were found.
publisher: Lawrence Erlbaum Associates
contributor: Forbus, Kenneth
contributor: Gentner, Dedre
contributor: Regier, Terry
date: 2004
type: Conference Paper
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/3779/1/cogsci04_2.pdf
identifier:   Lemaire, Benoît and Denhière, Guy  (2004) Incremental Construction of an Associative Network from a Corpus.  [Conference Paper]     
relation: http://cogprints.org/3779/