Abstract
In the past, Maximum Entropy based language models were constrained by training data n-gram counts, topic estimates, and triggers. We will investigate the obtainable gains from imposing additional constraints related to linguistic clusters, such as parts of speech, semantic/syntactic word clusters, and semantic labels. It will be shown that there substantial profit is available provided the estimates use Gaussian a priori statistics.
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© 2007 Springer-Verlag Berlin Heidelberg
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Jelinek, F., Cui, J. (2007). Language Modeling with Linguistic Cluster Constraints. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2007. Lecture Notes in Computer Science(), vol 4629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74628-7_1
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DOI: https://doi.org/10.1007/978-3-540-74628-7_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74627-0
Online ISBN: 978-3-540-74628-7
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