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Language model (LM) adaptation is often achieved by combining a generic LM with a topic-specific model that is more relevant to the target document. Unlike previous work on unsupervised LM adaptation, in this paper we propose to leverage named entity (NE) information for topic analysis and LM adaptation. We investigate two topic modeling approaches, latent Dirichlet allocation (LDA) and clustering,...
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