title: The Latent Relation Mapping Engine: Algorithm and Experiments
creator: Turney, Peter D.
subject: Language
subject: Computational Linguistics
subject: Semantics
subject: Machine Learning
subject: Artificial Intelligence
description: 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.
publisher: AI Access Foundation
date: 2008-12-22
type: Journal (Paginated)
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/6305/1/NRC-50738.pdf
identifier:   Turney, Peter D.  (2008) The Latent Relation Mapping Engine: Algorithm and Experiments.  [Journal (Paginated)]     
relation: http://cogprints.org/6305/