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.

本文介绍了一种新的基于语料库和潜在关系分析的类比生成算法 - LRME，它可以自动发现词语之间的语义关系来建立它们之间的类比，它在二十个类比映射问题上实现了人类水平的性能，并且相对于其他方法有更好的表现。