  The paper proposes a computationally feasible method for measuring
context-sensitive semantic distance between words. The distance is computed by
adaptive scaling of a semantic space. In the semantic space, each word in the
vocabulary V is represented by a multi-dimensional vector which is obtained
from an English dictionary through a principal component analysis. Given a word
set C which specifies a context for measuring word distance, each dimension of
the semantic space is scaled up or down according to the distribution of C in
the semantic space. In the space thus transformed, distance between words in V
becomes dependent on the context C. An evaluation through a word prediction
task shows that the proposed measurement successfully extracts the context of a
text.
