Abstract
This paper discusses a robust language model consisting of context-free grammar rules and symbol bigrams, integrated into a single framework. The aim is to remove the sharp grammatical/ungrammatical distinction by exploiting whatever grammar structure is present in every sentence, and hence to achieve continuity of scoring across the language. Both training and scoring are based on a similar principle: summing over paths that span the sentence. In addition to finding the overall score, a procedure for finding the best interpretation is described. Efficiency is maximised by the use of node-based (rather than path-based) procedures.
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© 1994 Springer-Verlag Berlin Heidelberg
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Wright, J.H., Jones, G.J.F., Lloyd-Thomas, H. (1994). Training and application of integrated grammar/bigram language models. In: Carrasco, R.C., Oncina, J. (eds) Grammatical Inference and Applications. ICGI 1994. Lecture Notes in Computer Science, vol 862. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58473-0_153
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DOI: https://doi.org/10.1007/3-540-58473-0_153
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