title: Semantic distance norms computed from an electronic dictionary (WordNet).
abstract: WordNet, an electronic dictionary (or lexical database), is a valuable resource for computational and cognitive scientists. Recent work on the computing of semantic distances among nodes (synsets) in WordNet has made it possible to build a large database of semantic distances for use in selecting word pairs for psychological research. The database now contains nearly 50,000 pairs of words that have values for semantic distance, associative strength, and similarity based on co-occurrence. Semantic distance was found to correlate weakly with these other measures but to correlate more strongly with another measure of semantic relatedness, featural similarity. Hierarchical clustering analysis suggested that the knowledge structure underlying semantic distance is similar in gross form to that underlying featural similarity. In experiments in which semantic similarity ratings were used, human participants were able to discriminate semantic distance. Thus, semantic distance as derived from WordNet appears distinct from other measures of word pair relatedness and is psychologically functional. This database may be downloaded from www.psychonomic.org/archive/.
authors.affiliation: Department of Psychology, Texas Tech University, Lubbock, TX 79409, USA. bill.maki@ttu.edu
authors.forename: William S
authors.initials: WS
authors.lastname: Maki
authors.forename: Lauren N
authors.initials: LN
authors.lastname: McKinley
authors.forename: Amber G
authors.initials: AG
authors.lastname: Thompson
mesh_terms: D019336:Cognitive Science / Q000295:instrumentation; D004014:Dictionaries as Topic; D006801:Humans; D012016:Reference Values; D012660:Semantics