PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Polynomial Algorithm for the Inference of Context Free Languages
Alexander Clark, Rémi Eyraud and Amaury Habrard
In: 9th International Colloquium on Grammatical Inference (ICGI 2008), Saint Malo, France(2008).

Abstract

We present a polynomial algorithm for the inductive inference of a large class of context free languages, that includes all regular languages. The algorithm uses a representation which we call Binary Feature Grammars based on a set of features, capable of representing richly structured context free languages as well as some context sensitive languages. More precisely, we focus on a particular case of this representation where the features correspond to contexts appearing in the language. Using the paradigm of positive data and a membership oracle, we can establish that all context free languages that satisfy two constraints on the context distributions can be identified in the limit by this approach. The polynomial time algorithm we propose is based on a generalisation of distributional learning and uses the lattice of context occurrences. The formalism and the algorithm seem well suited to natural language and in particular to the modelling of first language acquisition.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Theory & Algorithms
ID Code:4477
Deposited By:Amaury Habrard
Deposited On:13 March 2009