Learning Analogies and Semantic Relations
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 Scholastic Aptitude Test (SAT). 
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). We motivate this research by 
relating it to work in cognitive science and linguistics, and 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 these challenging problems.