  There are at least two kinds of similarity. 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. 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, and
information retrieval. Recently the Vector Space Model (VSM) of information
retrieval has been adapted to 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, (2) the Singular Value Decomposition
(SVD) is used to smooth the frequency data, and (3) automatically generated
synonyms are used to explore variations 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 semantic relations, LRA achieves
similar gains over the VSM.
