| LREC 2000 2nd International Conference on Language Resources & Evaluation | ||||||
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| Title | Using Lexical Semantic Knowledge from Machine Readable Dictionaries for Domain Independent Language Modelling |
| Authors | Demetriou George (Department of Computer Science, University of Sheffield, 211 Portobello Street, Sheffield S1 4DP, United Kingdom, G.Demetriou@dcs.shef.ac.uk) Atwell Eric (School of Computer Studies, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, United Kingdom, eric@scs.leeds.ac.uk) Souter Clive (School of Computer Studies, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, United Kingdom, cs@scs.leeds.ac.uk) |
| Keywords | Language Modelling, Lexical Semantics, Machine Readable Dictionaries, Speech Recognition |
| Session | Session WO11 - Mono-Multilingual Lexicon Acquisition and Building |
| Full Paper | 357.ps, 357.pdf |
| Abstract | Machine Readable Dictionaries (MRDs) have been used in a variety of language processing tasks including word sense disambiguation, text segmentation, information retrieval and information extraction. In this paper we describe the utilization of semantic knowledge acquired from an MRD for language modelling tasks in relation to speech recognition applications. A semantic model of language has been derived using the dictionary definitions in order to compute the semantic association between the words. The model is capable of capturing phenomena of latent semantic dependencies between the words in texts and reducing the language ambiguity by a considerable factor. The results of experiments suggest that the semantic model can improve the word recognition rates in “noisy-channel” applications. This research provides evidence that limited or incomplete knowledge from lexical resources such as MRDs can be useful for domain independent language modelling. |