Semantic distance norms computed from an electronic dictionary (WordNet) |
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Authors: | William S Maki Lauren N McKinley Amber G Thompson |
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Institution: | (1) Department of Psychology, Texas Tech University, 79409 Lubbock, TX |
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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 fromwww.psychonomic.org/archive/. |
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Keywords: | |
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