Modeling semantic memory: Effects of presenting semantic information in different modalities

Modeling Semantic Memory

Computational models of semantics infer semantic structure from the analysis of large linguistic corpora. Two approaches are described here. In each case the data consist of a document-by-word matrix, counting the number of times a word was used in a document. Latent Semantic Analysis uses dimension reduction to construct a high-dimensional semantic space from such a matrix. Information in sema...

Marijuana Users' Semantic System of Addiction: Presenting a Grounded Theory

Objective: The present research aimed to discover the semantic system of addiction from the perspective of marijuana users. Method: The research method was grounded theory in which semi-structured interviews were conducted with 20 young people in the city of Rasht who had a history of marijuana use, and the data were collected by theoretical-purposive sampling and analyzed with open, axial, and...

Semantic Richness Effects in Memory

The semantic richness of a word is multidimensional, and includes dimensions such as semantic neighborhood density, imageability, number of features, and valence. While certain dimensions (e.g., imageability) have been examined in the memory domain, the bulk of semantic richness research has been confined to visual word recognition tasks. Therefore, it is unclear if other dimensions influence m...

A semantic space for modeling children's semantic memory

A preliminary model based on a child corpus In contrast to many LSA semantic spaces in the literature which are based on domainspecific corpora, we chose to build a general child corpus intended to model children's semantic memory (Denhière, Lemaire, Bellissens & JheanLarose, 2004) and to offer a layer of basic semantic associations on top of which computational models of children cognitive pro...

Recognition of meeting actions using information obtained from different modalities - a semantic approach