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We present an approach for the detection of coordinate-term relationshipsbetween entities from the software domain, that refer to Java classes. Usually,relations are found by examining corpus statistics associated with textentities. In some technical domains, however, we have access to additionalinformation about the real-world objects named by the entities, suggesting thatcoupling information about the "grounded" entities with corpus statistics mightlead to improved methods for relation discovery. To this end, we develop asimilarity measure for Java classes using distributional information about howthey are used in software, which we combine with corpus statistics on thedistribution of contexts in which the classes appear in text. Using ourapproach, cross-validation accuracy on this dataset can be improveddramatically, from around 60% to 88%. Human labeling results show that ourclassifier has an F1 score of 86% over the top 1000 predicted pairs.

Grounded Discovery of Coordinate Term Relationships between Software Entities