arXiv cs/03110452003-11-27cs.AI

多重对齐、统一和搜索信息压缩框架下的无监督语法归纳

Unsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search

J Gerard Wolff

发表信息：: Proceedings of the Workshop and Tutorial on Learning Context-Free Grammars (in association with the 14th European Conference on Machine Learning and the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2003), September 2003, Cavtat-Dubrovnik, Croata), editors: C. de la Higuera and P. Adriaans and M. van Zaanen and J. Oncina, pp 113-124

摘要：

本文描述了一种新的语法归纳方法，该方法是在一个框架内开发的，该框架旨在将学习与计算，人工智能，数学和逻辑的其他方面相结合。这个框架被称为“多重对齐、统一和搜索的信息压缩”（ICMAUS），它建立在Solomonoff等人开创的最小长度编码的原则之上。本文的大部分内容描述了SP70，这是ICMAUS框架的一个计算机模型，它包含了语法的无监督学习过程。一个例子来展示该模型如何从适当的输入中推断出一个合理的语法。简要讨论了当前模型的局限性以及如何克服这些局限性。

英文摘要：

This paper describes a novel approach to grammar induction that has been developed within a framework designed to integrate learning with other aspects of computing, AI, mathematics and logic. This framework, called "information compression by multiple alignment, unification and search" (ICMAUS), is founded on principles of Minimum Length Encoding pioneered by Solomonoff and others. Most of the paper describes SP70, a computer model of the ICMAUS framework that incorporates processes for unsupervised learning of grammars. An example is presented to show how the model can infer a plausible grammar from appropriate input. Limitations of the current model and how they may be overcome are briefly discussed.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

相关论文