Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation
<|reference_start|>Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation: 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).<|reference_end|>
@article{florian2001dynamic,
    title={Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based
  Adaptation},
    author={Radu Florian and David Yarowsky},
    journal={Proceedings of the 37th Annual Meeting of the ACL, pages 167-174,
  College Park, Maryland},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0104019},
    primaryClass={cs.CL}
}