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Adaptive language modeling with a set of domain dependent models

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Abstract

An adaptive language modeling method is proposed in this paper. Instead of using one static model for all situations, it applies a set of specific models to dynamically adapt to the discourse. We present the general structure of the model and the training procedure. In our experiments, we instantiated the method with a set of domain dependent models which are trained according to different socio-situational settings (almosd). We compare it with previous topic dependent and socio-situational setting dependent adaptive language models and with a smoothed n-gram model in terms of perplexity and word prediction accuracy. Our experiments show that almosd achieves perplexity reductions up to almost 12% compared with the other models. © 2012 Springer-Verlag.
Original languageEnglish
Title of host publicationText, Speech and Dialogue - 15th International Conference, TSD 2012, Proceedings
Pages472-479
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event15th International Conference on Text, Speech and Dialogue, TSD 2012 - , Czech Republic
Duration: 3 Sept 20127 Sept 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Text, Speech and Dialogue, TSD 2012
Country/TerritoryCzech Republic
Period3/09/127/09/12

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