Modelling (sub)string-length based constraints through a grammatical inference method
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
An efficient algorithm for the inference of circuit-free automata
Syntactic and structural pattern recognition
String matching with left-to-right networks
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Error-Correcting Viterbi Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Finite-State Machines-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning finite-state models for machine translation
Machine Learning
Learning Balls of Strings with Correction Queries
ECML '07 Proceedings of the 18th European conference on Machine Learning
Learning Balls of Strings from Edit Corrections
The Journal of Machine Learning Research
A bibliographical study of grammatical inference
Pattern Recognition
String distances and uniformities
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
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In many language processing tasks, most of the sentences generally convey rather simple meanings. Moreover, these tasks have a limited semantic domain that can be properly covered with a simple lexicon and a restricted syntax. Nevertheless, casual users are by no means expected to comply with any kind of formal syntactic restrictions due to the inherent “spontaneous” nature of human language. In this work, the use of error-correcting-based learning techniques is proposed to cope with the complex syntactic variability which is generally exhibited by natural language. In our approach, a complex task is modeled in terms of a basic finite state model, F, and a stochastic error model, E. F should account for the basic (syntactic) structures underlying this task, which would convey the meaning. E should account for general vocabulary variations, word disappearance, superfluous words, and so on. Each “natural” user sentence is thus considered as a corrupted version (according to E) of some “simple” sentence of L(F). Adequate bootstrapping procedures are presented that incrementally improve the “structure” of F while estimating the probabilities for the operations of E. These techniques have been applied to a practical task of moderately high syntactic variability, and the results which show the potential of the proposed approach are presented.