Towards Incremental Learning of Mildly Context-Sensitive Grammars

Most models in grammatical inference have been restricted to regular or context-free grammars. As a step towards learning of more powerful grammars, this paper discusses the incremental learning of Linear Indexed Grammars (LIGs) for formal languages from positive and negative sample strings. We implemented methods of learning LIGs in LIG Learner system. An important feature of LIG Learner is incremental learning through rule generation mechanism called bridging and a search for rule sets. This paper reports experimental results on learning several LIGs for fundamental mildly-context-sensitive languages including the copy language, or the set of strings of the form ww, and a set of strings representing pseudo-knots in modeling RNA, as well as some RIR (right-linear-indexed right-linear) grammars, which are restricted LIGs and equivalent to context-free grammars.