An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
Journal of Automata, Languages and Combinatorics - Special issue: Selected papers of the workshop weighted automata: Theory and applications (Dresden University of Technology (Germany), March 4-8, 2002)
Applying Probability Measures to Abstract Languages
IEEE Transactions on Computers
Learning Rational Stochastic Tree Languages
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Relevant Representations for the Inference of Rational Stochastic Tree Languages
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Relevant Representations for the Inference of Rational Stochastic Tree Languages
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
A spectral approach for probabilistic grammatical inference on trees
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
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Recently, an algorithm - DEES - was proposed for learning rational stochastic tree languages. Given a sample of trees independently and identically drawn according to a distribution defined by a rational stochastic language, DEES outputs a linear representation of a rational series which converges to the target. DEES can then be used to identify in the limit with probability one rational stochastic tree languages. However, when DEES deals with finite samples, it often outputs a rational tree series which does not define a stochastic language. Moreover, the linear representation can not be directly used as a generative model. In this paper, we show that any representation of a rational stochastic tree language can be transformed in a reduced normalised representation that can be used to generate trees from the underlying distribution. We also study some properties of consistency for rational stochastic tree languages and discuss their implication for the inference. We finally consider the applicability of DEES to trees built over an unranked alphabet.