Joerg P. Ueberla

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Statistical language models frequently suffer from a lack of training data. This problem can be alleviated by clustering, because it reduces the number of free parameters that need to be trained. However, clustered models have the following drawback: if there is " enough " data to train an unclustered model, then the clustered variant may perform worse. On(More)
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(More)
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