Y. Bengio and P. Frasconi. An input output HMM architecture. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems, volume 7, pages 427-434. The MIT Press, 1995. URL citeseer.nj.nec.com/bengio95input.html.

Google Scholar

[3]

O. Cicchello. A new limited search approach of learning Abbadingo-style finite state automata. PhD thesis, University of Guelph, 2002.

Google Scholar

[4]

O. Cicchello and S. C. Kremer. Beyond EDSM. In ICGI, pages 37-48, 2002a.

Crossref

Google Scholar

[5]

O. Cicchello and S. C. Kremer. Constrained search for Abbadingo-style problems, 2002b. Under Review.

Google Scholar

[6]

F. Coste and J. Nicolas. Regular inference as a graph coloring problem. In Workshop on Grammatical Inference, Automata Induction, and Language Acquisition (ICML '97), Nashville, TN, 1997. URL citeseer.nj.nec.com/coste97regular.html.

Google Scholar

[7]

P. Dupont. Regular grammatical inference from positive and negative samples by genetic search: the GIG method. In ICGI, pages 236-245, 1994. URL citeseer.nj.nec.com/dupont94regular.html.

Crossref

Google Scholar

[8]

M. L. Forcada and R. C. Carrasco. Learning the initial state of a Second-Order Recurrent Neural Network during regular-language inference. Neural Computation, 7(5):923-930, September 1995. URL citeseer.nj.nec.com/forcada94learning.html.

Crossref

Google Scholar

[9]

Y. Freund, M. Kearns, D. Ron, R. Rubinfeld, R. Shapire, and L. Sellie. Efficient learning of typical finite automata from random walks. In Proceedings of the Twenty-fifth Annual Symposium on Theory of Computing, pages 315-324, 1993.

Crossref

Google Scholar

[10]

C. L. Giles, D. Chen, C. B. Miller, H. H. Chen, G. Z. Sun, and Y. C. Lee. Second-order recurrent neural networks for grammatical inference. In IEEE INNS International Joint Conference on Neural Networks, volume II, pages 273-281, 1991.

Google Scholar

[11]

E. M. Gold. Language identification in the limit. Information and Control, 10:447-474, 1967.

Google Scholar

[12]

E. M. Gold. Complexity of automaton identification from given data. Information and Control, 37: 302-320, 1978.

Google Scholar

[13]

M. A. Harrison. Introduction to formal language theory, Addison-Wesley, Reading, Mass., 1978.

Crossref

Google Scholar

[14]

J. E. Hopcroft and J. D. Ullman. Formal Languages and their Relation to Automata. Addison-Wesley, Reading, Mass., 1969.

Crossref

Google Scholar

[15]

H. Juillé and J. B. Pollack. A sampling-based heuristic for tree search applied to grammar induction. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98) Tenth Conference on Innovative Applications of Artificial Intelligence (IAAI-98), Madison, Wisconsin, USA, 26-30 1998a. AAAI Press Books. URL citeseer.nj.nec.com/juille98samplingbased.html.

Crossref

Google Scholar

[16]

H. Juillé and J. B. Pollack. A stochastic search approach to grammar induction. In ICGI, pages 126-137, 1998b. URL citeseer.nj.nec.com/juille98stochastic.html.

Crossref

Google Scholar

[17]

K. J. Lang. Random DFA's can be approximately learned from sparse uniform examples. In Proceedings of the Fifth ACM Workshop on Computational Learning Theory, pages 45-52, New York, N.Y., 1992. ACM. URL citeseer.nj.nec.com/lang92random.html.

Crossref

Google Scholar

[18]

K. J. Lang. Evidence-driven state merging with search, 1998. URL citeseer.nj.nec.com/lang98evidence.html. K.J. Lang, (1998) Evidence-Driven State Merging with Search, NECI Technical Report TR98-139.

Google Scholar

[19]

K. J. Lang. Faster algorithms for finding minimal consistent DFAs, 1999. URL citeseer.nj.nec.com/353128.html. NEC Tech. Report.

Google Scholar

[20]

K. J. Lang and B. A. Pearlmutter. Abbadingo one: Dfa learning competition, 1997. URL http://abbadingo.cs.unm.edu/.

Google Scholar

[21]

K. J. Lang, B. A. Pearlmutter, and F. Coste. Gowachin, 1998a. URL http://www.irisa.fr/Gowachin/.

Google Scholar

[22]

K. J. Lang, B. A. Pearlmutter, and R. A. Price. Results of the abbadingo one DFA learning competition and a new evidence-driven state merging algorithm. vLecture Notes in Computer Science, 1433: 1-12, 1998b. URL citeseer.nj.nec.com/article/lang98results.html.

Crossref

Google Scholar

[23]

S. Luke, S. Hamahashi, and H. Kitano. Genetic programming. In Proceedings of the 1999 Genetic and Evolutionary Computation Conference (GECC099). Morgan Kaufmann, 1999.

Google Scholar

[24]

A. L. Oliveira and S. Edwards. Limits of exact algorithms for inference of minimum size finite state machines. In Algorithmic Learning Theory, 7th International Workshop, ALT '96, Sydney, Australia, October 1996, Proceedings, volume 1160, pages 59-66. Springer, 1996. URL citeseer.nj.nec.com/63994.html.

Crossref

Google Scholar

[25]

A. L. Oliveira and J. P. Marques Silva. Efficient search techniques for the inference of minimum size finite automata. In String Processing and Information Retrieval, pages 81-89, 1998. URL citeseer.nj.nec.com/oliveira97efficient.html.

Google Scholar

[26]

J. M. Pena and A. L. Oliveira. A new algorithm for the reduction of incompletely specified finite state machines. In ICCAD, pages 482-489, 1998. URL citeseer.nj.nee.com/pena98new.html.

Crossref

Google Scholar

[27]

L. Pitt. Inductive Inference, DFAs, and Computational Complexity. 1304 W. Springfield Avenue, Urbana, IL 61801, July 1989. Supported in part by NSF grant IRI-88-09570, and by the Deparment of Computer Science, University of Illinois at Urbana-Champaign.

Google Scholar

[28]

J. B. Pollack. The induction of dynamical recognizers. Machine Learning, 7:227-252, 1991. URL citeseer.nj.nee.com/pollack91induction.html.

Crossref

Google Scholar

[29]

S. Russel and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice-Hall, 1996.

Crossref

Google Scholar

[30]

M. Tomita. Learning Of Construction Of Finite Automata From Examples Using Hill-Climbing. Pittsburgh, Pennsylvania 15213, May 1982. Sponsored by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 3597, monitored by the Air Force Avionics Laboratory Under Contract F33615-81-K-1539.

Google Scholar

[31]

B. A. Trakhtenbrot and Y. M. Barzdin. Finite Automata Behavior and Synthesis. North-Holland Publishing Company, Amsterdam, 1973.

Google Scholar

Cited By

View all

Gao YWang MYu B(2022)Dynamic Specification Mining Based on TransformerTheoretical Aspects of Software Engineering10.1007/978-3-031-10363-6_16(220-237)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-10363-6_16
Mu JGoodman NRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Emergent communication of generalizationsProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541638(17994-18007)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3541638
Bartoli ADe Lorenzo AMedvet ETarlao F(2016)Regex-based entity extraction with active learning and genetic programmingACM SIGAPP Applied Computing Review10.1145/2993231.299323216:2(7-15)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.1145/2993231.2993232
Show More Cited By

Index Terms

Inducing grammars from sparse data sets: a survey of algorithms and results
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning
2. General and reference
  1. Document types
    1. Surveys and overviews

Recommendations

Inducing Tree-Substitution Grammars

Inducing a grammar from text has proven to be a notoriously challenging learning task despite decades of research. The primary reason for its difficulty is that in order to induce plausible grammars, the underlying model must be capable of representing ...
Semantics and algorithms for data-dependent grammars
POPL '10: Proceedings of the 37th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages

We present the design and theory of a new parsing engine, YAKKER, capable of satisfying the many needs of modern programmers and modern data processing applications. In particular, our new parsing engine handles (1) full scannerless context-free ...
Semantics and algorithms for data-dependent grammars
POPL '10

We present the design and theory of a new parsing engine, YAKKER, capable of satisfying the many needs of modern programmers and modern data processing applications. In particular, our new parsing engine handles (1) full scannerless context-free ...

Comments

Information & Contributors

Information

Published In

cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 4, Issue

12/1/2003

1486 pages

ISSN:1532-4435

EISSN:1533-7928

Issue’s Table of Contents

Publisher

JMLR.org

Publication History

Published: 01 December 2003

Published in JMLR Volume 4

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

26
Total Citations
View Citations
820
Total Downloads

Downloads (Last 12 months)59
Downloads (Last 6 weeks)7

Reflects downloads up to 16 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Gao YWang MYu B(2022)Dynamic Specification Mining Based on TransformerTheoretical Aspects of Software Engineering10.1007/978-3-031-10363-6_16(220-237)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-10363-6_16
Mu JGoodman NRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Emergent communication of generalizationsProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541638(17994-18007)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3541638
Bartoli ADe Lorenzo AMedvet ETarlao F(2016)Regex-based entity extraction with active learning and genetic programmingACM SIGAPP Applied Computing Review10.1145/2993231.299323216:2(7-15)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.1145/2993231.2993232
Bartoli ADe Lorenzo AMedvet ETarlao FOssowski S(2016)Active learning approaches for learning regular expressions with genetic programmingProceedings of the 31st Annual ACM Symposium on Applied Computing10.1145/2851613.2851668(97-102)Online publication date: 4-Apr-2016
https://dl.acm.org/doi/10.1145/2851613.2851668
Bartoli ADe Lorenzo AMedvet ETarlao F(2016)Inference of Regular Expressions for Text Extraction from ExamplesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.251558728:5(1217-1230)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1109/TKDE.2016.2515587
Bartoli ADe Lorenzo AMedvet ETarlao FVirgolin MEsparcia-Alcázar A(2015)Evolutionary Learning of Syntax Patterns for Genic Interaction ExtractionProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754706(1183-1190)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754706
Bartoli ADe Lorenzo AMedvet ETarlao FArnold D(2014)Playing regex golf with genetic programmingProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598333(1063-1070)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598333
Stevenson ACordy J(2014)A survey of grammatical inference in software engineeringScience of Computer Programming10.1016/j.scico.2014.05.00896:P4(444-459)Online publication date: 15-Dec-2014
https://dl.acm.org/doi/10.1016/j.scico.2014.05.008
Pałka DZachara M(2011)Learning web application firewall - benefits and caveatsProceedings of the IFIP WG 8.4/8.9 international cross domain conference on Availability, reliability and security for business, enterprise and health information systems10.5555/2033973.2033999(295-308)Online publication date: 22-Aug-2011
https://dl.acm.org/doi/10.5555/2033973.2033999
Sundarakantham KSheena NShalinie S(2010)SSGL: a semi-supervised grammar learnerInternational Journal of Computer Applications in Technology10.1504/IJCAT.2010.03452638:4(259-263)Online publication date: 1-Aug-2010
https://dl.acm.org/doi/10.1504/IJCAT.2010.034526
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Index Terms

Recommendations

Inducing Tree-Substitution Grammars

Semantics and algorithms for data-dependent grammars

Semantics and algorithms for data-dependent grammars

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations