  This paper introduces Latent Relational Analysis (LRA), a method for
measuring relational similarity. LRA has potential applications in many areas,
including information extraction, word sense disambiguation, machine
translation, and information retrieval. Relational similarity is correspondence
between relations, in contrast with attributional similarity, which is
correspondence between attributes. When two words have a high degree of
attributional similarity, we call them synonyms. When two pairs of words have a
high degree of relational similarity, we say that their relations are
analogous. For example, the word pair mason/stone is analogous to the pair
carpenter/wood. Past work on semantic similarity measures has mainly been
concerned with attributional similarity. Recently the Vector Space Model (VSM)
of information retrieval has been adapted to the task of measuring relational
similarity, achieving a score of 47% on a collection of 374 college-level
multiple-choice word analogy questions. In the VSM approach, the relation
between a pair of words is characterized by a vector of frequencies of
predefined patterns in a large corpus. LRA extends the VSM approach in three
ways: (1) the patterns are derived automatically from the corpus (they are not
predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the
frequency data (it is also used this way in Latent Semantic Analysis), and (3)
automatically generated synonyms are used to explore reformulations of the word
pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent
to the average human score of 57%. On the related problem of classifying
noun-modifier relations, LRA achieves similar gains over the VSM, while using a
smaller corpus.
