  We present an algorithm for learning from unlabeled text, based on the Vector
Space Model (VSM) of information retrieval, that can solve verbal analogy
questions of the kind found in the SAT college entrance exam. A verbal analogy
has the form A:B::C:D, meaning "A is to B as C is to D"; for example,
mason:stone::carpenter:wood. SAT analogy questions provide a word pair, A:B,
and the problem is to select the most analogous word pair, C:D, from a set of
five choices. The VSM algorithm correctly answers 47% of a collection of 374
college-level analogy questions (random guessing would yield 20% correct; the
average college-bound senior high school student answers about 57% correctly).
We motivate this research by applying it to a difficult problem in natural
language processing, determining semantic relations in noun-modifier pairs. The
problem is to classify a noun-modifier pair, such as "laser printer", according
to the semantic relation between the noun (printer) and the modifier (laser).
We use a supervised nearest-neighbour algorithm that assigns a class to a given
noun-modifier pair by finding the most analogous noun-modifier pair in the
training data. With 30 classes of semantic relations, on a collection of 600
labeled noun-modifier pairs, the learning algorithm attains an F value of 26.5%
(random guessing: 3.3%). With 5 classes of semantic relations, the F value is
43.2% (random: 20%). The performance is state-of-the-art for both verbal
analogies and noun-modifier relations.
