LREC 2000 - Papers

LREC 2000 2^nd 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

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.

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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
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.