On Using Selectional Restriction in Language Models for Speech Recognition

Details On Using Selectional Restriction in Language Models for Speech Recognition On Using Selectional Restriction in Language Models for Speech Recognition

1994-08-19

arxiv.org/abs/cmp-lg/9408010v1

Computer Science

Computation and Language

feedback is welcome to ueberla@cs.sfu.ca

Scientific paper

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.

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