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• W1987181863 abstract "Traditional methods for deriving property-based representations of concepts from text have focused on either extracting only a subset of possible relation types, such as hyponymy/hypernymy (e.g., car is-a vehicle) or meronymy/metonymy (e.g., car has wheels), or unspecified relations (e.g., car--petrol). We propose a system for the challenging task of automatic, large-scale acquisition of unconstrained, human-like property norms from large text corpora, and discuss the theoretical implications of such a system. We employ syntactic, semantic, and encyclopedic information to guide our extraction, yielding concept-relation-feature triples (e.g., car be fast, car require petrol, car cause pollution), which approximate property-based conceptual representations. Our novel method extracts candidate triples from parsed corpora (Wikipedia and the British National Corpus) using syntactically and grammatically motivated rules, then reweights triples with a linear combination of their frequency and four statistical metrics. We assess our system output in three ways: lexical comparison with norms derived from human-generated property norm data, direct evaluation by four human judges, and a semantic distance comparison with both WordNet similarity data and human-judged concept similarity ratings. Our system offers a viable and performant method of plausible triple extraction: Our lexical comparison shows comparable performance to the current state-of-the-art, while subsequent evaluations exhibit the human-like character of our generated properties." @default.
• W1987181863 created "2016-06-24" @default.
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• W1987181863 date "2013-11-06" @default.
• W1987181863 modified "2023-10-16" @default.
• W1987181863 title "Automatic Extraction of Property Norm‐Like Data From Large Text Corpora" @default.
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• W1987181863 doi "https://doi.org/10.1111/cogs.12091" @default.
• W1987181863 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25019134" @default.
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