On Using Selectional Restriction in Language Models for Speech Recognition

Joerg P. Ueberla

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arXiv · cmp-lg/9408010

On Using Selectional Restriction in Language Models for Speech Recognition

Joerg P. Ueberla

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Abstract

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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Joerg P. Ueberla. 1994-08-19. On Using Selectional Restriction in Language Models for Speech Recognition. https://arxiv.org/abs/cmp-lg/9408010

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