Open AccessLayerFolding
Author(s) -
Ping Xiao,
Hannu Toivonen
Publication year - 2016
Publication title -
helda (university of helsinki)
Language(s) - English
Resource type - Conference proceedings
ISBN - 978-1-4503-3739-7
DOI - 10.1145/2851613.2851883
Subject(s) - computer science , node (physics) , set (abstract data type) , recall , association (psychology) , artificial intelligence , word (group theory) , natural language processing , similarity (geometry) , theoretical computer science , image (mathematics) , mathematics , psychology , cognitive psychology , engineering , geometry , structural engineering , programming language , psychotherapist
A frequent challenge in creative tasks such as advertising is finding novel and concrete representations of abstract concepts. We cast this problem as finding, in word association networks, the relevant indirect associations of a given node. We propose a novel approach, LayerFolding, which selects nodes at increasing distances from the given node, according to their relatedness to it. The relatedness is calculated based on the shortest paths that are potentially coherent. In a test against a small set of visual representations of abstract concepts found in real advertisements, LayerFolding provides a 79% recall, and outperforms other two popular semantic relatedness measures.
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