The latent relation mapping engine: algorithm and experiments

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AuthorSearch for:
TypeArticle
Journal titleJournal of Artificial Intelligence Research
Volume33
Pages615655; # of pages: 41
AbstractMany AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for handcoded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance.
Publication date
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21277320
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Record identifier0131399a-0d18-4b6c-9ef5-2ffdcd99ed4f
Record created2016-03-08
Record modified2016-05-09
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