Learning alignment profiles for structural similarity measure

Abstract

Synthesis of context-free grammar based on alignment profile similarity from input sample texts of the unknown target language is studied. Among researches in artificial intelligence, learning context-free grammars from sample strings is a fundamental and important subject, since it is a basic means for defining natural language and for syntactic pattern recognition. Alignment profiles guide the induction of grammar, and hence better alignment profiles improve the accuracy of learned grammars. In this paper, we introduce a scheme for learning alignment profiles from input texts. Several examples are presented to illustrate the scheme and its behavior. © 2012 IEEE.