Detecting verbal participation in diathesis alternations

We present a method for automatically identifying verbal participation in diathesis alternations. Automatically acquired subcategorization frames are compared to a hand-crafted classification for selecting candidate verbs. The minimum description length principle is then used to produce a model and cost for storing the head noun instances from a training corpus at the relevant argument slots. Alternating subcategorization frames are identified where the data from corresponding argument slots in the respective frames can be combined to produce a cheaper model than that produced if the data is encoded separately.