A distributional assessment of rivalry in word formation

We contrast two views of rivalry in word formation. Under the classical, categorical view, two processes are rivals if they are semantically equivalent. Under the more nuanced, gradient view, two processes can be rivals at different degrees, depending on how frequently they are amenable to be deployed as alternatives to one another. We propose to use methods from distributional semantics to explore the usefulness of both views. Building on data from French, we first show that distributional differences between average difference vectors capture semantic similarity across derivational processes in a manner comparable to the expectations of expert morphologists. We then propose an operational implementation of the classical view of rivalry based on computational classifiers: processes are rivals if and only if a classifier is unable to discriminate between them. Experimentation with French data shows that this operationalization correctly captures the broad brushes of rivalry, but also reveals finer, gradient aspects of competition in the spirit of gradient rivalry.