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Automatic extraction of property norm-like data from large text corpora.

Colin Kelly, Barry Devereux, Anna Korhonen

    Cognitive Science
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    This study introduces a novel system for automatically extracting human-like properties of concepts from large text collections. The method effectively generates concept-relation-feature triples, demonstrating comparable performance to state-of-the-art approaches.

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    Area of Science:

    • Natural Language Processing
    • Computational Linguistics
    • Cognitive Science

    Background:

    • Traditional methods extract limited relation types (hyponymy, meronymy) or unspecified relations.
    • Acquiring unconstrained, human-like property norms from text at scale is challenging.

    Purpose of the Study:

    • To develop a system for automatic, large-scale acquisition of unconstrained, human-like property norms from text corpora.
    • To explore the theoretical implications of such a system for conceptual representations.

    Main Methods:

    • Utilized syntactic, semantic, and encyclopedic information for guided extraction.
    • Extracted concept-relation-feature triples from parsed corpora (Wikipedia, British National Corpus).
    • Employed syntactically and grammatically motivated rules, reweighting triples by frequency and statistical metrics.

    Main Results:

    • System generates concept-relation-feature triples approximating property-based conceptual representations.
    • Lexical comparison shows performance comparable to state-of-the-art.
    • Human evaluations confirm the human-like character of the generated properties.

    Conclusions:

    • The proposed system offers a viable and performant method for plausible triple extraction.
    • The approach advances the automatic acquisition of conceptual knowledge from text.
    • Findings have implications for computational models of human cognition and knowledge representation.