  In this paper, we investigate the use of selectional restriction -- the
constraints a predicate imposes on its arguments -- in a language model for
speech recognition. We use an un-tagged corpus, followed by a public domain
tagger and a very simple finite state machine to obtain verb-object pairs from
unrestricted English text. We then measure the impact the knowledge of the verb
has on the prediction of the direct object in terms of the perplexity of a
cluster-based language model. The results show that even though a clustered
bigram is more useful than a verb-object model, the combination of the two
leads to an improvement over the clustered bigram model.
