On Using Selectional Restriction in Language Models for Speech Recognition
| dc.creator | Ueberla, Joerg P. | |
| dc.date | 1994-08-19 | |
| dc.date.accessioned | 2026-07-07T09:09:28Z | |
| dc.date.available | 2026-07-07T09:09:28Z | |
| dc.description | 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. | |
| dc.description | feedback is welcome to ueberla@cs.sfu.ca | |
| dc.identifier | https://arxiv.org/abs/cmp-lg/9408010 | |
| dc.identifier | http://arxiv.org/abs/cmp-lg/9408010 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/151049 | |
| dc.subject | Computation and Language | |
| dc.title | On Using Selectional Restriction in Language Models for Speech Recognition | |
| dc.type | text |