Handbook of formal languages, vol. 3
Determinization of finite state weighted tree automata
Journal of Automata, Languages and Combinatorics
Finite-state transducers in language and speech processing
Computational Linguistics
Simpler and more general minimization for weighted finite-state automata
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Backward and forward bisimulation minimization of tree automata
Theoretical Computer Science
Minimizing deterministic weighted tree automata
Information and Computation
Weight pushing and binarization for fixed-grammar parsing
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
The myhill-nerode theorem for recognizable tree series
DLT'03 Proceedings of the 7th international conference on Developments in language theory
Bisimulation minimisation for weighted tree automata
DLT'07 Proceedings of the 11th international conference on Developments in language theory
Efficient inference through cascades of weighted tree transducers
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Every sensible extended top-down tree transducer is a multi bottom-up tree transducer
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
| Hi-index | 0.00 |
Explicit pushing for weighted tree automata over semifields is introduced. A careful selection of the pushing weights allows a normalization of bottom-up deterministic weighted tree automata. Automata in the obtained normal form can be minimized by a simple transformation into an unweighted automaton followed by unweighted minimization. This generalizes results of Mohri and Eisner for deterministic weighted string automata to the tree case. Moreover, the new strategy can also be used to test equivalence of two bottom-up deterministic weighted tree automata M1 and M2 in time O(|M| log|Q|), where |M| = |M1| + |M2| and |Q| is the sum of the number of states of M1 and M2. This improves the previously best running time O(|M1| ċ |M2|).