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Context-free grammar induction with grammar-based classifier system

100%

Unold O.

2005

tom Vol. 15, no. 4

681-690

In the paper we deal with the induction of context-free grammars from sample sentences. We present some extensions to grammar-based classifier system (GCS) that evolves population of classifiers represented by context-free grammar productions rules in Chomsky Normal Form. GCS is a new version of Learning Classifier Systems but it differs from it in the covering, in the matching, and in representation. We modify the discovering component of the GCS and apply system for inferring such context-free languages as toy language, and grammar for large corpora of part-of-speech tagged natural English language. For each task a set of experiments was performed.

Inferring graph grammars by detecting overlap in frequent subgraphs

100%

Kukluk J. , Holder L. , Cook D.

nr 2

241-250

In this paper we study the inference of node and edge replacement graph grammars. We search for frequent subgraphs and then check for an overlap among the instances of the subgraphs in the input graph. If the subgraphs overlap by one node, we propose a node replacement graph grammar production. If the subgraphs overlap by two nodes or two nodes and an edge, we propose an edge replacement graph grammar production. We can also infer a hierarchy of productions by compressing portions of a graph described by a production and then inferring new productions on the compressed graph. We validate the approach in experiments where we generate graphs from known grammars and measure how well the approach infers the original grammar from the generated graph. We show graph grammars found in biological molecules, biological networks, and analyze learning curves of the algorithm.