Data mining for creating accentuation rules

Applied Artificial Intelligence

Volumn 18, Issue 5, 2004, Pages 395-410

  Sef, Tomaž ^a   Gams, Matjaž ^a  

^a JO EF STEFAN INSTITUTE   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA STRUCTURES; DATABASE SYSTEMS; LEARNING SYSTEMS; PROBLEM SOLVING; SPEECH ANALYSIS; SPEECH RECOGNITION;

ACCENTUATION RULES; FUNCTION MAPPING; STRESS ASSIGNMENT;

DATA MINING;

EID: 2942544308     PISSN: 08839514     EISSN: None     Source Type: Journal    
DOI: 10.1080/08839510490442049     Document Type: Article

References (24)

1. Black A., K. Lenzo, and V. Pagel. 1998. Issues in building general letter to sound rules. In Proceedings of 3rd ESCA Workshop on Speech Synthesis (ICSLP '98 Satellite Activities), pages 77-80, Jenolan Caves, Australia.
2. Busser, B., W. Daelemans, and A. van den Bosch. 1999. Machine learning of word pronunciation: The case against abstraction. In Proceedings of the Sixth European Conference on Speech Communication and Technology (EUROSPEECH '99), pages 2123-2126, Budapest, Hungary.
3. Daelemans, W. M. P., and A. P. J. van den Bosch. 1996. Language-independent data-oriented-grapheme-to-phoneme Conversion. In Progress in Speech Synthesis, eds. J. P. H. van Santen, R. W. Sproat, J. P. Olive, and J. Hirschberg, 77-89. New York: Springer.
4. Dasarathy, B. V. (ed.) 1991. Nearest Neighbor (Nn) Norms: Nn Pattern Classification Techniques. Los Alamitos, CA: IEEE Computer Society Press.
5. Dietterich, T. G., H. Hild, and G. Bakiri. 1995. A comparison of ID3 and backpropagation for English text-to-speech mapping. Machine Learning 19(1):5-28.
6. Erjavec, T., and N. Ide. 1998. The MULTEXT-East Corpus. In Proceedings of First International Conference on Language Resources & Evaluation (LREC '98), pages 28-30, Granada, Spain.
7. Gams, M. 2001. Weak Intelligence: Through the Principle and Paradox of Multiple Knowledge. Advances in Computation: Theory and Practice, Volume 6. New York: Nova Science Publishers, Inc.
8. Gams, M., M. Drobnič, and N. Karba. 1996. Average-case improvements when integrating ML and KA. Applied Intelligence 6:87-99.
9. Holte, R. C., L. E. Acker, and B. W. Porter. 1989. Concept learning and the problem of small disjuncts. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89). pages 813-818, Detroit, MI, USA.
10. Mitchell, T. M. 1997. Machine Learning. New York: McGraw Hill Text.
11. Mladenič, D. 2001. EU project: Data mining and decision support for business competativeness: A European virtual enterprise (Sol-Eu-Net). In Proceedings of Open Enterprise Solutions Systems, Experiences and Organizations (OES-SEO 2001), pages 172-173, Rome, Italy.
12. Quinlan, J. R. 1986. Induction of decision tress. Machine Learning 1(1):81-106.
13. Quinlan, J. R. 1996. Improved use of continuous attributes in CA. 5. Journal of Artificial Intelligence Research 4:77-90.
14. Quinlan, J. R. 2002. RuleQuest Research. http://www.rulequest.com/ contact.html.
15. SAZU. 1990. Slovene Orthography - 1. Rules (Slovenski pravopis - 1. Pravila). Ljubljana: Državna založba Slovenije.
16. SAZU. 1991. A Dictionary of the Slovene literary language (Slovar slovenskega knjižnega jezika). Ljubljana: Drǎavna založba Slovenije.
17. Sejnowski, T. J., and C. S. Rosenberg. 1987. Parallel networks that learn to pronounce English text. Complex Systems 1:145-168.
18. Šef, T. 2001. Text Analysis for the Slovenian Text-to-Speech Synthesis System. Ph.D. Thesis, Faculty of Computer and Information Science, University of Ljubljana.
19. Škrjanc, M., M. Grobelnik, and M. Zupanič. 2001. Insights offered by data-mining when analyzing media space data. Informatica 25(3):357-363.
20. Škrjanc, M., T. Šef, and M. Gams. 2002. Using decision tree for accentuation in the Slovenian language. In Proceedings of Starting Artificial Intelligence Researchers Sympasium (STAIRS 2002), pages 135-144, Lyon, France.
21. Sp̀roat, R. (ed.) 1998. Multilingual Text-to-Speech Synthesis: The Bell Labs Approach. Boston: Kluwer Academic Publishers.
22. Toporišič, J. 1984. Slovene Grammar (Slovenska slovnica). Maribor: Založba Obzorja Maribor.
23. Trappl, R. (ed.) 1986. Impacts of Artificial Intelligence: Scientific, Technological, Military, Economic, Societal, Cultural, and Political. Amsterdam: North Holland.
24. Weiss, G. M., and H. Hirsh. 2000. A quantitative study of small disjuncts. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000), pages 665-670, Menlo Park, CA, USA.