Class-based variable memory length Markov model
Class-based variable memory length Markov model
复制标题
基于类的可变内存长度马尔可夫模型
DOI：
10.21437/interspeech.2005-6
复制
发表时间：
2005
期刊：
影响因子：
7.5
通讯作者：
Gakuto Kurata
中科院分区：
文献类型：
--
作者：
Shinsuke Mori;Gakuto Kurata
本文提出了一种基于类的变记忆长度马尔可夫模型及其学习算法。这是可变记忆长度马尔可夫模型的一个扩展。我们的模型是基于一个类为基础的概率后缀树，其节点有一个自动获取的词类关系。我们通过实验将我们的新模型与基于词的二元模型、基于词的三元模型、基于类的二元模型和基于词的可变记忆长度马尔可夫模型进行了比较。结果表明，基于类的变记忆长度马尔可夫模型在复杂度和模型规模方面优于其他模型。
In this paper, we present a class-based variable memory length Markov model and its learning algorithm. This is an extension of a variable memory length Markov model. Our model is based on a class-based probabilistic suffix tree, whose nodes have an automatically acquired wordclass relation. We experimentally compared our new model with a word-based bi-gram model, a word-based tri-gram model, a class-based bi-gram model, and a word-based variable memory length Markov model. The results show that a class-based variable memory length Markov model outperforms the other models in perplexity and model size.