  Knowing the degree of semantic contrast between words has widespread
application in natural language processing, including machine translation,
information retrieval, and dialogue systems. Manually-created lexicons focus on
opposites, such as {\rm hot} and {\rm cold}. Opposites are of many kinds such
as antipodals, complementaries, and gradable. However, existing lexicons often
do not classify opposites into the different kinds. They also do not explicitly
list word pairs that are not opposites but yet have some degree of contrast in
meaning, such as {\rm warm} and {\rm cold} or {\rm tropical} and {\rm
freezing}. We propose an automatic method to identify contrasting word pairs
that is based on the hypothesis that if a pair of words, $A$ and $B$, are
contrasting, then there is a pair of opposites, $C$ and $D$, such that $A$ and
$C$ are strongly related and $B$ and $D$ are strongly related. (For example,
there exists the pair of opposites {\rm hot} and {\rm cold} such that {\rm
tropical} is related to {\rm hot,} and {\rm freezing} is related to {\rm
cold}.) We will call this the contrast hypothesis. We begin with a large
crowdsourcing experiment to determine the amount of human agreement on the
concept of oppositeness and its different kinds. In the process, we flesh out
key features of different kinds of opposites. We then present an automatic and
empirical measure of lexical contrast that relies on the contrast hypothesis,
corpus statistics, and the structure of a {\it Roget}-like thesaurus. We show
that the proposed measure of lexical contrast obtains high precision and large
coverage, outperforming existing methods.

Computation and Language