Communications of the ACM
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward efficient agnostic learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The minimum consistent DFA problem cannot be approximated within any polynomial
Journal of the ACM (JACM)
An introduction to computational learning theory
An introduction to computational learning theory
Learning bias and phonological-rule induction
Computational Linguistics
The String-to-String Correction Problem
Journal of the ACM (JACM)
Inference of Reversible Languages
Journal of the ACM (JACM)
Automata, Languages, and Machines
Automata, Languages, and Machines
Finite-State Language Processing
Finite-State Language Processing
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Data Structures and Algorithms
Data Structures and Algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inductive Inference, DFAs, and Computational Complexity
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Finite-state transducers in language and speech processing
Computational Linguistics
Inference of string mappings for language technology
Inference of string mappings for language technology
Re-engineering letter-to-sound rules
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
A statistical text-to-phone function using ngrams and rules
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
| Hi-index | 0.00 |
Local deterministic string-to-string transductions arise in natural language processing (NLP) tasks such as letter-to-sound translation or pronunciation modeling. This class of transductions is a simple generalization of morphisms of free monoids; learning local transductions is essentially the same as inference of certain monoid morphisms. However, learning even a highly restricted class of morphisms, the so-called fine morphisms, leads to intractable problems: deciding whether a hypothesized fine morphism is consistent with observations is an NP-complete problem; and maximizing classification accuracy of the even smaller class of alphabetic substitution morphisms is APX-hard. These theoretical results provide some justification for using the kinds of heuristics that are commonly used for this learning task.