Bayesian grammar induction for language modeling
Stanley F. Chen · 1995
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.