内部外部算法

内部外部算法（英语：inside-outside algorithm）是一种重新检验随机上下文无关文法（probabilistic context-free grammar）生成几率的方式，由James K. Baker 于1979年提出，是一个一般化的向前向后算法，用来作为随机上下文无关文法其隐马尔可夫模型的属性评估。这种算法是用来计算某种期望值，举例来说，可以用来成为最大期望算法（一种无监督的学习算法）的一部分。

参考资料

• J. Baker (1979): Trainable grammars for speech recognition. In J. J. Wolf and D. H. Klatt, editors, Speech communication papers presented at the 97th meeting of the Acoustical Society of America, pages 547–550, Cambridge, MA, June 1979. MIT.
• Karim Lari, Steve J. Young (1990): The estimation of stochastic context-free grammars using the inside-outside algorithm. Computer Speech and Language, 4:35–56.
• Karim Lari, Steve J. Young (1991): Applications of stochastic context-free grammars using the Inside-Outside algorithm. Computer Speech and Language, 5:237-257.
• Fernando Pereira, Yves Schabes (1992): Inside-outside reestimation from partially bracketed corpora. Proceedings of the 30th annual meeting on Association for Computational Linguistics, Association for Computational Linguistics, 128-135.
• Christopher D. Manning, Heinrich Shütze (1999): Foundations of statistical natural language processing.

外部链接

• An implementation of the algorithm by Mark Johnson
• [1] （页面存档备份，存于互联网档案馆）
• [2]