Abstract
Term extraction is the task of automatically detecting, from textual corpora, lexical units that designate concepts in thematically restricted domains (e.g. medicine). Current systems for term extraction integrate linguistic and statistical cues to perform the detection of terms. The best results have been obtained when some kind of combination of simple base term extractors is performed [14]. In this paper it is shown that this combination can be further improved by posing an additional learning problem of how to find the best combination of base term extractors. Empirical results, using AdaBoost in the metalearning step, show that the ensemble constructed surpasses the performance of all individual extractors and simple voting schemes, obtaining significantly better accuracy figures at all levels of recall.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Ananiadou, S.: A Methodology for Automatic Term Recognition. In Proceedings of the 15th International Conference on Computational Linguistics, COLING, pages 1034–1038, Kyoto, Japan, 1994.
Abney, S., Schapire, R.E. and Singer, Y.: Boosting Applied to Tagging and PP—attachment. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, EMNLP-VLC, pages 38–45, College Park, MD, 1999.
Bourigault, D.: LEXTER, un Logiciel d’EXtraction de TERminologie. Application à l’acquisition des connaissances à partir de textes. Phd. Thesis, École des Hautes Études en Sciences Sociales, Paris, 1994.
Carreras, X. and Màrquez, L.: Boosting Trees for Clause Splitting. To appear in Proceedings of the 5th Conference on Computational Natural Language Learning, CoNLL’01, Tolouse, France, 2001.
Daille, B.: Approche mixte pour l’extraction de terminologie: statistique lexicale et filtres linguistiques. Phd. Thesis, Université Paris VII, 1994.
Escudero, G; Màrquez, L. and Rigau, G.: Boosting Applied to Word Sense Disambiguation. In Proceedings of the 12th European Conference on Machine Learning, ECML, Barcelona, Spain, 2000.
Justeson, J. and Katz, S.: Technical Terminology: Some Linguistic Properties and an Algorithm for Identification in Text. Natural Language Engineering,1(1), 1994.
Kageura, K. and Umino, B.: Methods for Automatic Term Recognition: A Review. Terminology, 3(2): 259–289, 1996.
Kittler, J.; Hatef, M.; Duin, R. and Matas, J.: On Combining Classifiers. IEEE Transations on Pattern Analysis and Machine Intelligence, 20(3):226–238, 1998.
Magnini, B. and Cavaglia, G.: Integrating Subject Field Codes into WordNet. Proceedings of the 2nd International Conference on Language resources and Evaluation, LREC2000, Atenas.
Maynard, D.: Term Recognition Using Combined Knowledge Sources. Phd. Thesis, Manchester Metropolitan Univ., Faculty of Science and Engineering, 1999.
Schapire, R.E. and Singer, Y.: Improved Boosting Algorithms Using Confidence-rated Predictions. Machine Learning, 37(3):297–336, 1999.
Schapire, R.E. and Singer, Y.: BoosTexter: A Boosting-based System for Text Categorization. Machine Learning, 39(2/3):135–168, 2000.
Vivaldi, J. and Rodríguez, H.: Improving Term Extraction by Combining Different Techniques. In Proceedings of the Workshop on Computational Terminology for Medical and Biological Applications, pages 61–68, Patras, Greece, 2000.
Vivaldi, J.: A Multistrategy Approach to Term Candidate Extraction. Phd. Thesis (forthcoming). Dep. LSI, Technical University of Catalonia, Barcelona, 2001
Vossen, P. (ed.): EuroWordNet: A Multilingual Database with Lexical Semantic Networks. Kluwer Academic Publishers, Dordrecht, 1998.
Wolpert, D. H.: Stacked Generalization. Neural Networks, Pergamon Press, 5:241–259, 1992.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vivaldi, J., Màrquez, 2., Rodríguez, H. (2001). Improving Term Extraction by System Combination Using Boosting. In: De Raedt, L., Flach, P. (eds) Machine Learning: ECML 2001. ECML 2001. Lecture Notes in Computer Science(), vol 2167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44795-4_44
Download citation
DOI: https://doi.org/10.1007/3-540-44795-4_44
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42536-6
Online ISBN: 978-3-540-44795-5
eBook Packages: Springer Book Archive