Bayesian Grammar Induction for Language Modeling
<|reference_start|>Bayesian Grammar Induction for Language Modeling: We describe a corpus-based induction algorithm for probabilistic context-free grammars. The algorithm employs a greedy heuristic search within a Bayesian framework, and a post-pass using the Inside-Outside algorithm. We compare the performance of our algorithm to n-gram models and the Inside-Outside algorithm in three language modeling tasks. In two of the tasks, the training data is generated by a probabilistic context-free grammar and in both tasks our algorithm outperforms the other techniques. The third task involves naturally-occurring data, and in this task our algorithm does not perform as well as n-gram models but vastly outperforms the Inside-Outside algorithm.<|reference_end|>
@article{chen1995bayesian,
    title={Bayesian Grammar Induction for Language Modeling},
    author={Stanley F. Chen (Harvard University)},
    journal={Proc. 33rd Annual Meeting of the ACL, p. 228-235, Cambridge, MA
  1995},
    year={1995},
    archivePrefix={arXiv},
    eprint={cmp-lg/9504034},
    primaryClass={cmp-lg cs.CL}
}