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Ensemble Learning of Economic Taxonomy Relations from Modern Greek Corpora

  • Katia Lida Kermanidis

This paper proposes the use of ensemble learning for the identification of taxonomic relations between Modern Greek economic terms. Unlike previous approaches, apart from is-a and part-of relations, the present work deals also with relation types that are characteristic of the economic domain. Semantic and syntactic information governing the term pairs is encoded in a novel feature-vector representation. Ensemble learning helps overcome the problem of performance instability and leads to more accurate predictions.

Keywords

Semantic Similarity Base Classifier Base Learner Ensemble Learn Candidate Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bay, S.: Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets. In Proc. of the 15th International Conference on Machine Learning (1998) 37–45Google Scholar
  2. 2.
    Breiman, L.: Bagging predictors. Machine Learning24(1996):123–140MATHMathSciNetGoogle Scholar
  3. 3.
    Cimiano, P., Hotho, A., Staab., S.: Comparing Conceptual, Divisive and Agglomerative Clustering fro Learning Taxonomies from Text. Proceedings of the European Conference on Artificial Intelligence (2004). Valencia, SpainGoogle Scholar
  4. 4.
    Degeratu, M., Hatzivassiloglou, V.: An Automatic Model for Constructing Domain-Specific Ontology Resources. Proceedings of the International Conference on Language Resources and Evaluation (2004): 2001– 2004. Lisbon, PortugalGoogle Scholar
  5. 5.
    Dietterich, T.: Ensemble Learning. Tha Handbook of Brain Theory and Neural Networks. Second Edition. Cambridge MA: The MIT Press (2002)Google Scholar
  6. 6.
    Faure, D., Nedellec., C.: A Corpus-based Conceptual Clustering Method for Verb Frames and Ontology. Proceedings of the LREC Workshop on Adapting Lexical and Corpus Resources to Sublanguages and Applications (1998). Granada, SpainGoogle Scholar
  7. 7.
    Freund, Y., Schapire, R. E.: Experiments with a new boosting algorithm. Proceedings of the International Conference on Machine Learning (1996): 148– 156. San FranciscoGoogle Scholar
  8. 8.
    Hatzigeorgiu, N., Gavrilidou, M., Piperidis, S., Carayannis, G., Papakostopoulou, A., Spiliotopoulou, A., Vacalopoulou, A., Labropoulou, P., Mantzari, E., Papageorgiou, H., Demiros, I.: Design and Implementation of the online ILSP Greek Corpus. Proceedings of the 2nd International Conference on Language Resources and Evaluation (2000): 1737– 1742. Athens, GreeceGoogle Scholar
  9. 9.
    Hearst, M. A.: Automatic Acquisition of Hyponyms from Large Text Corpora. Proceedings of the International Conference on Computational Linguistics (1992): 539– 545. Nantes, FranceGoogle Scholar
  10. 10.
    Kermanidis, K., Fakotakis, N., Kokkinakis, G.: DELOS: An Automatically Tagged Economic Corpus for Modern Greek. Proceedings of the Third International Conference on Language Resources and Evaluation (2002): 93– 100. Las Palmas de Gran Canaria, SpainGoogle Scholar
  11. 11.
    Lendvai, P.: Conceptual Taxonomy Identification in Medical Documents. Proceedings of the Second International Workshop on Knowledge Discovery and Ontologies (2005): 31– 38. Porto, PortugalGoogle Scholar
  12. 12.
    Maedche, A., Volz, R.: The Ontology Extraction and Maintenance Framework Text-To-Onto. Proceedings of the Workshop on Integrating Data Mining and Knowledge Mining (2001). San Jose, CaliforniaGoogle Scholar
  13. 13.
    Makagonov, P., Figueroa, A. R., Sboychakov, K., Gelbukh, A.: Learning a Domain Ontology from Hierarchically Structured Texts. Proceedings of the 22nd International Conference on Machine Learning (2005). Bonn, GermanyGoogle Scholar
  14. 14.
    Manning, C., Schuetze., H.: Foundations of Statistical Natural Language Processing. MIT Press (1999)Google Scholar
  15. 15.
    Navigli, R., Velardi, P.: Learning Domain Ontologies from Document Warehouses and Dedicated WebSites. Computational Linguistics, 50(2). MIT Press (2004)Google Scholar
  16. 16.
    Partners of ESPRIT-291/860,: Unification of the Word Classes of the ESPRIT Project 860. Internal Report BU-WKL-0376 (1986)Google Scholar
  17. 17.
    Opitz, D., Maclin, R.: Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research Vol. 11 (1999) 169– 198MATHGoogle Scholar
  18. 18.
    Pekar, V., Staab. S.: Taxonomy Learning — Factoring the Structure of a Taxonomy into a Semantic Classification Decision. Proceedings of the International Conference on Computational Linguistics (2002). Taipei, TaiwanGoogle Scholar
  19. 19.
    Pereira, F., Tishby, N., Lee, L.: Distributional Clustering of English Words. Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics (1993)Google Scholar
  20. 20.
    Platt, J.: Fast Training of Support Vector Machines using Sequential Minimal Optimization. Advances in Kernel Methods - Support Vector Learning (1998), B. Schoelkopf, C. Burges, and A. Smola, eds. MIT Press.Google Scholar
  21. 21.
    Quinlan, R.: C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA (1993)Google Scholar
  22. 22.
    Schapire, R. E., Rochery, M., Rahim, M., Gupta, N.: Incorporating prior knowledge into boosting. Proceedings of the Nineteenth International Conference on Machine Learning (2002)Google Scholar
  23. 23.
    Thanopoulos, A., Kermanidis, K., Fakotakis, N.: Challenges in Extracting Terminology from Modern Greek Texts. Proceedings of the Workshop on Text-based Information Retrieval (2006). Riva del Garda, ItalyGoogle Scholar
  24. 24.
    Witschel, H. F.: Using Decision Trees and Text Mining Techniques for Extending Taxonomies. Proceedings of the Workshop on Learning and Extending Lexical Ontologies by Using Machine Learning Methods (2005Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of InformaticsIonian UniversityCorfuGreece

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