Algorithms for learning regular expressions from positive data

Information and Computation

Volumn 207, Issue 4, 2009, Pages 521-541

  Fernau, Henning ^a  

^a UNIVERSITY OF TRIER   (Germany)

Author keywords

Identification in the limit from positive data; Regular expressions; Text learning

Indexed keywords

FINITE AUTOMATA;

DETERMINISTIC FINITE AUTOMATON; IDENTIFICATION IN THE LIMIT FROM POSITIVE DATA; REGULAR EXPRESSIONS; REGULAR LANGUAGES; TEXT LEARNING;

LEARNING ALGORITHMS;

EID: 60849094038     PISSN: 08905401     EISSN: 10902651     Source Type: Journal    
DOI: 10.1016/j.ic.2008.12.008     Document Type: Article

References (55)

1. Ahonen H. Disambiguation of SGML content models. In: Nicholas C., and Wood D. (Eds). Principles of Document Processing PODP'96, volume 1293 of LNCS (1997), Springer 27-37
2. Ahonen H., Mannila H., and Nikunen E. Forming grammars for structured documents: an application of grammatical inference. In: Carrasco R.C., and Oncina J. (Eds). Proceedings of the Second International Colloquium on Grammatical Inference (ICGI-94): Grammatical Inference and Applications, volume 862 of LNCS/LNAI (1994), Springer 153-167
3. Angluin D. Inference of reversible languages. Journal of the ACM 29 (1982) 741-765
4. H. Behme, S. Mintert (translators), Extensible Markup Language (XML) 1.0; Anhang E. Available from: .
5. Berstel J., and Boasson L. Formal properties of XML grammars and languages. Acta Informatica 38 (2002) 649-671
6. Bertoni A., Choffrut C., and Palano B. Context-free grammars and XML languages. In: Ibarra O.H., and Dang Z. (Eds). Developments in Language Theory DLT, volume 4036 of LNCS (2006), Springer 108-119
7. Bex G.J., Neven F., Schwentick T., and Tuyls K. Inference of concise DTDs from XML data. In: Dayal U., Whang K.-Y., Lomet D.B., Alonso G., Lohman G.M., Kersten M.L., Cha S.K., and Kim Y.-K. (Eds). 32nd International Conference on Very Large Data Bases VLDB (2006), ACM Press 115-126
8. Blackwell A.F. SWYN: A visual representation for regular expressions. In: Lieberman H.. (Ed). Your wish is my command: giving users the power to instruct their software (2001), Morgan Kaufmann 245-270
9. Blattner M., and Cremers A. Observations about bounded languages and developmental systems. Theory of Computing Systems 10 (1976) 253-258
10. Brazma A. Inductive synthesis of dot expons. In: Barzdins J., and Bjørner D. (Eds). Baltic Computer Science, volume 502 of LNCS (1991), Springer 156-212
11. A. Brazma, Efficient identification of regular expressions from representative examples, in: Proc. of the Ann. Conf. on Computational Learning Theory COLT, ACM, 1993, pp. 236-242.
12. Brazma A. Learning a subclass of regular expressions by recognizing periodic repetitions. In: Sandewall E., and Jansson C.G. (Eds). Proc. of the 4th Scandinavian Conference on Artificial Intelligence (1993), IOS Press 137-146
13. A. Brazma, Learning of regular expressions by pattern matching, in: P.M.B. Vitányi, (Eds.), Computational Learning Theory, Second European Conference, EuroCOLT, volume 904 of LNCS/LNAI, Springer, 1995, pp. 392-403.
14. Brazma A. Efficient learning of regular expressions from approximate examples. In: Greiner R., Petsche T., and Hanson S.J. (Eds). Computational Learning Theory and Natural Learning Systems, volume IV of Making Learning Systems Practical (1997), MIT Press, Cambridge, MA 337-352 chapter 19
15. Brazma A., and Kinber E. Generalized regular expressions-a language for synthesis of programs with branching in loops. Theoretical Computer Science 46 (1986) 175-195
16. Brüggemann-Klein A. Unambiguity of extended regular expressions in SGML document grammars. In: Lengauer T. (Ed). European Symposium on Algorithms ESA, volume 726 of LNCS (1993), Springer 73-84
17. Brüggemann-Klein A., and Wood D. One-unambiguous regular languages. Information and Computation 142 (1998) 182-206
18. Chung Y.D., Kim J.W., and Kim M.H. Efficient preprocessing of XML queries using structured signatures. Information Processing Letters 87 (2003) 257-264
19. R. Cox, Regular expression matching can be simple and fast, January 2007. Available from .
20. CZ-Redaktion, Maschinenmenschen plaudern per XML mit der Unternehmens-IT, Computer Zeitung, Heft 50:30, December 2000.
21. Dupont P. Incremental regular inference. In: Miclet L., and de la Higuera C. (Eds). Proceedings of the Third International Colloquium on Grammatical Inference (ICGI-96): Learning Syntax from Sentences, volume 1147 of LNCS/LNAI (1996), Springer 222-237
22. P. Dupont, L. Miclet, Inférence grammaticale régulière: fondements théoriques et principaux algorithmes, Technical Report RR-3449, INRIA, 1998.
23. Fernau H. Learning XML grammars. In: Perner P. (Ed). Machine Learning and Data Mining in Pattern Recognition MLDM'01, volume 2123 of LNCS/LNAI (2001), Springer 73-87
24. Fernau H. Identification of function distinguishable languages. Theoretical Computer Science 290 (2003) 1679-1711
25. H. Fernau, Algorithms for learning regular expressions, in: S. Jain, H.-U. Simon, E. Tomita (Eds.), Algorithmic Learning Theory ALT 2005, volume 3734 of LNCS/LNAI, 2005, pp. 297-311.
26. H. Fernau, J.M. Sempere, Permutations and control sets for learning non-regular language families, in: A.L. Oliveira (Ed.), Grammatical Inference: Algorithms and Applications, 5th International Colloquium (ICGI 2000), volume 1891 of LNCS/LNAI, Springer, 2000.
27. Fulk M.A. Prudence and other conditions on formal language learning. Information and Computation (formerly Information and Control) 85 (1990) 1-11
28. Garofalakis M., Gionis A., Rastogi R., Seshadri S., and Shim K. XTRACT: learning document type descriptors from XML document collections. Data Mining and Knowledge Discovery 7 (2003) 23-56
29. Giammmarresi D., and Montalbano R. Deterministic generalized automata. Theoretical Computer Science 215 (1999) 191-208
30. Gold E.M. Language identification in the limit. Information and Control (now Information and Computation) 10 (1967) 447-474
31. Gruber H., and Holzer M. Finite automata, digraph connectivity, and regular expression size (extended abstract). In: Aceto L., Damgård I., Goldberg L.A., Halldórsson M.M., Ingólfsdóttir A., and Walukiewicz I. (Eds). Automata, Languages and Programming ICALP (2), volume 5126 of LNCS (2008), Springer 39-50
32. C. de la Higuera, Grammatical inference, July 2007. Available from: .
33. O. Hofmann, Regular expressions and other pattern discovery methods. Does it have to be so complicated? Bioinformatics Honours Course, 18th March 2005. Available from: .
34. Hopcroft J.E., and Ullman J.D. Introduction to Automata Theory, Languages, and Computation (1979), Addison-Wesley, Reading (MA)
35. Kinber E. Learning a class of regular expressions via restricted subset queries. In: Jantke K.P. (Ed). Analogical and Inductive Inference AII, volume 642 of LNCS/LNAI (1992), Springer 232-243
36. E. Kinber, J. Nessel, Learning regular expressions from examples and queries. Personal communication, 2005.
37. E.B. Kinber, On learning regular expressions and patterns via membership and correction queries, in: A. Clark, F. Coste, L. Miclet (Eds.), Grammatical Inference: Algorithms and Applications, 9th International Colloquium, ICGI, volume 5278 of LNCS, Springer, 2008, pp. 125-138.
38. Korenblit M., and Levit V.E. On algebraic expressions of series-parallel and Fibonacci graphs. In: Calude C., Dinneen M.J., and Vajnovszki V. (Eds). Discrete Mathematics and Theoretical Computer Science DMTCS, volume 2731 of LNCS (2003), Springer 215-224
39. Laird Ph.D. Learning from Good and Bad Data (1988), Kluwer Academic Publishers, Norwell, MA, USA
40. S. Lange, T. Zeugmann, Types of monotonic language learning and their characterization, in: Computational Learning Theory COLT, ACM Press, 1992, pp. 377-390.
41. Lange S., and Zeugmann T. Incremental learning from positive data. Journal of Computer and System Sciences 53 (1996) 88-103
42. Lange S., and Zeugmann T. Set-driven and rearrangement-independent learning of recursive languages. Mathematical Systems Theory 29 (1996) 599-634
43. da Luz R., Ferrari M.F., and Musicante M.A. Regular expression transformations to extend regular languages (with applications to a Datalog XML schema validator). Journal of Algorithms 62 (2007) 148-167
44. C.G. Nevill-Manning, I.H. Witten, Online and offline heuristics for inferring hierarchies of repetitions in sequences. Proc. IEEE, 88 (2000) 1745-1755.
45. In: Rozenberg G., and Salomaa A. (Eds). Handbook of Formal Languages vol. III (1997), Springer, Berlin
46. G. Schäfer-Richter, Über Eingabeabhängigkeit und Komplexität von Inferenzstrategien. Rheinisch-Westfälische Technische Hochschule Aachen, Dissertation, 1984.
47. Sempere J.M. On a class of regular-like expressions for linear languages. Journal of Automata, Languages and Combinatorics 5 (2000) 343-354
48. T.C. Smith, I.H. Witten, J.G. Cleary, S. Legg, Objective evaluation of inferred context-free grammars, in: Proc. 2nd Australian and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia, Institute of Electrical and Electronics Engineers, November 1994, pp. 392-396.
49. Vilo J., Brazma A., Jonassen I., Robinson A.J., and Ukkonen E. Mining for putative regulatory elements in the yeast genome using gene expression data. In: Bourne P.E., Gribskov M., Altman R.B., Jensen N., Hope D., Lengauer T., Mitchell J.C., Scheeff E.D., Smith C., Strande S., and Weissig H. (Eds). Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology ISMB (2000), AAAI Press 384-394
50. Wang L., and Jiang T. On the complexity of multiple sequence alignment. Journal of Computational Biology 1 (1994) 337-348
51. Watt A. Beginning Regular Expressions (2005), Wiley
52. Wiehagen R. From inductive inference to algorithmic learning theory. New Generation Computing 12 (1994) 321-335
53. M. van Zaanen, Bootstrapping Structure into Language: Alignment-Based Learning. PhD, School of Computing, University of Leeds, UK, September 2001.
54. Zeugmann T. Can learning in the limit be done efficiently?. In: Gavaldà R., Jantke K.P., and Takimoto E. (Eds). Algorithmic Learning Theory ALT, volume 2842 of LNCS (2003), Springer 17-38
55. T. Zeugmann, S. Lange, A guided tour across the boundaries of learning recursive languages, in: K.P. Jantke, S. Lange (Eds.), Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report, volume 961 of LNCS, Springer, 1995, pp. 190-258.