Reclassifying subcategorization frames for experimental analysis and stimulus generation

Researchers in the fields of psycholinguistics and neurolinguistics increasingly test their experimental hypotheses against probabilistic models of language. VALEX (Korhonen, Krymolowski & Briscoe, 2006) is a large-scale verb lexicon that specifies verb usage as probability distributions over a set of 163 verb SUBCATEGORIZATION FRAMES (SCFs). VALEX has proved to be a popular computational linguistic resource and may also be used by psychoand neurolinguists for experimental analysis and stimulus generation. However, a probabilistic model based upon a set of 163 SCFs often proves too fine grained for experimenters in these fields. Our goal is to simplify the classification by grouping the frames into genera---explainable clusters that may be used as experimental parameters. We adopted two methods for re-classification. One was a manual, linguistic approach derived from verb argumentation and clause features; the other was an automatic, computational approach driven from the graphical representation of SCFs for use in Natural Language Processing technology. The premise was not only to compare the results of two quite different methods for our own interest, but also to enable other researchers to choose whichever re-classification better suited their purpose (one being grounded purely in theoretical linguistics and the other in practical language engineering). The various classifications are available as a free online resource to researchers.