Learning Cover Context-Free Grammars from Structural Data

Learning Cover Context-Free Grammars from Structural Data

We consider the problem of learning an unknown context-free grammar when the only knowledge available and of interest to the learner is about its structural descriptions with depth at most l. The goal is to learn a cover context-free grammar (CCFG) with respect to l, that is, a CFG whose structural descriptions with depth at most l agree with those of the unknown CFG. We propose an algorithm, c...

Efficient Learning of Context-Free Grammars from Positive Structural Examples

In this paper, we introduce a new normal form for context-free grammars, called reversible context-free grammars, for the problem of learning context-free grammars from positive-only examples. A context-free grammar G = (N, Z, P, S) is said to be reversible if (1) A + G( and B -+ a in P implies A = B and (2) A -+ a@ and A --f aCfl in P implies B = C. We show that the class of reversible context...

Learning k-Reversible Context-Free Grammars from Positive Structural Examples

Learning context-free grammars from stochastic structural information

We consider the problem of learning context-free grammars from stochastic structural data. For this purpose, we have developed an algorithm (tlips) which identiies any rational tree set from stochastic samples and approximates the probability distribution of the trees in the language. The procedure identiies equivalent subtrees in the sample and outputs the hypothesis in linear time with the nu...

Learning context-free grammars to extract relations from text

In this paper we propose a novel relation extraction method, based on grammatical inference. Following a semisupervised learning approach, the text that connects named entities in an annotated corpus is used to infer a context free grammar. The grammar learning algorithm is able to infer grammars from positive examples only, controlling overgeneralisation through minimum description length. Eva...