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Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation

Radu Florian·David Yarowsky
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Abstract

This paper presents a novel method of generating and applying hierarchical, dynamic topic-based language models. It proposes and evaluates new cluster generation, hierarchical smoothing and adaptive topic-probability estimation techniques. These combined models help capture long-distance lexical dependencies. Experiments on the Broadcast News corpus show significant improvement in perplexity (10.5% overall and 33.5% on target vocabulary).

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BibTeXRIS

Radu Florian, David Yarowsky. 2001-04-27. Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation. https://arxiv.org/abs/cs/0104019

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