@misc{cogprints6305,
          volume = {33},
           month = {December},
          author = {Peter D. Turney},
            note = {NRC-50738},
           title = {The Latent Relation Mapping Engine: Algorithm and Experiments},
       publisher = {AI Access Foundation},
         journal = {Journal of Artificial Intelligence Research},
           pages = {615--655},
            year = {2008},
        keywords = {analogy, metaphor, semantic relations, structure mapping, vector space models, analogical mapping, latent relational analysis},
             url = {http://cogprints.org/6305/},
        abstract = {Many 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 hand-coded 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.}
}

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