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.
Original language | English |
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Pages (from-to) | 638-682 |
Number of pages | 45 |
Journal | Cognitive Science |
Volume | 38 |
Early online date | 06 Nov 2013 |
DOIs | |
Publication status | Published - 06 Nov 2014 |
Externally published | Yes |
Keywords
- Entropy
- Human evaluation
- Log-likelihood
- Natural language processing
- Pointwise mutual information
- Property norm
- Wikipedia
- WordNet
ASJC Scopus subject areas
- Language and Linguistics
- Experimental and Cognitive Psychology
- Cognitive Neuroscience
- Artificial Intelligence