  We present an approach for the detection of coordinate-term relationships
between entities from the software domain, that refer to Java classes. Usually,
relations are found by examining corpus statistics associated with text
entities. In some technical domains, however, we have access to additional
information about the real-world objects named by the entities, suggesting that
coupling information about the "grounded" entities with corpus statistics might
lead to improved methods for relation discovery. To this end, we develop a
similarity measure for Java classes using distributional information about how
they are used in software, which we combine with corpus statistics on the
distribution of contexts in which the classes appear in text. Using our
approach, cross-validation accuracy on this dataset can be improved
dramatically, from around 60% to 88%. Human labeling results show that our
classifier has an F1 score of 86% over the top 1000 predicted pairs.
