title: Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping.
abstract: The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in a nunsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.
authors.affiliation: ARC Key Centre for Human Factors and Applied Cognitive Pyschology, University of Queensland, Brisbane, Queensland, Australia. asmith@humanfactors.uq.edu.au
authors.forename: Andrew E
authors.initials: AE
authors.lastname: Smith
authors.forename: Michael S
authors.initials: MS
authors.lastname: Humphreys
mesh_terms: D004867:Equipment Design; D006801:Humans; D007802:Language; D008037:Linguistics / Q000295:instrumentation*; D012660:Semantics