Abstract
In the last years dependency parsing has been accomplished by machine learning–based systems showing great accuracy but usually under 90% for Labelled Attachment Score (LAS). Maltparser is one of such systems. Machine learning allows to obtain parsers for every language having an adequate training corpus. Since generally such systems can not be modified the following question arises: Can we beat this 90% LAS by using better training corpora? Some previous work points that high level techniques are not sufficient for building more accurate training corpora. Thus, by analyzing the words that are more frequently incorrectly attached or labelled, we study the feasibility of some low level techniques, based on n–version parsing models, in order to obtain better parsing accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Buchholz, S., Marsi, E.: CoNLL–X Shared Task on Multilingual Dependency Parsing. In: Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL–X), pp. 149–164 (2006)
Ballesteros, M., Herrera, J., Francisco, V., Gervás, P.: Improving Parsing Accuracy for Spanish using Maltparser. Journal of the Spanish Society for Natural Language Processing (SEPLN) 44 (in press, 2010)
Herrera, J., Gervás, P.: Towards a Dependency Parser for Greek Using a Small Training Data Set. Journal of the Spanish Society for Natural Language Processing (SEPLN) 41, 29–36 (2008)
Herrera, J., Gervás, P., Moriano, P.J., Moreno, A., Romero, L.: Building Corpora for the Development of a Dependency Parser for Spanish Using Maltparser. Journal of the Spanish Society for Natural Language Processing (SEPLN) 39, 181–186 (2007)
Herrera, J., Gervás, P., Moriano, P.J., Moreno, A., Romero, L.: JBeaver: un Analizador de Dependencias para el Español Basado en Aprendizaje. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds.) CAEPIA 2007. LNCS (LNAI), vol. 4788, pp. 211–220. Springer, Heidelberg (2007)
Nivre, J., Hall, J., Nilsson, J.: Memory–based Dependency Parsing. In: Proceedings of CoNLL–2004, Boston, MA, USA, pp. 49–56 (2004)
Eisner, J.: Three New Probabilistic Models for Dependency Parsing: An Exploration. In: Proceedings of the 16th International Conference on Computational Linguistics (COLING 1996), Copenhagen, pp. 340–345 (1996)
Yamada, H., Matsumoto, Y.: Statistical Dependency Analysis with Support Vector Machines. In: Proceedings of International Workshop of Parsing Technologies (IWPT 2003), pp. 195–206 (2003)
Palomar, M., Civit, M., Díaz, A., Moreno, L., Bisbal, E., Aranzabe, M., Ageno, A., Martí, M.A., Navarro, B.: 3LB: Construcción de una Base de Datos de Árboles Sintáctico–Semánticos para el Catalán, Euskera y Español. In: Proceedings of the XX Conference of the Spanish Society for Natural Language Processing (SEPLN), Sociedad Española para el Procesamiento del Lenguaje Natural, pp. 81–88 (2004)
Taulé, M., Martí, M., Recasens, M.: AnCora: Multilevel Annotated Corpora for Catalan and Spanish. In: Proceedings of 6th International Conference on Language Resources and Evaluation (2008)
McDonald, R., Lerman, K., Pereira, F.: Multilingual Dependency Analysis with a Two-Stage Discriminative Parser. In: Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL–X), pp. 216–220 (2006)
Nivre, J., Hall, J., Nilsson, J., Eryiğit, G., Marinov, S.: Labeled Pseudo–Projective Dependency Parsing with Support Vector Machines. In: Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL–X), pp. 221–225 (2006)
Johansson, R., Nugues, P.: Investigating Multilingual Dependency Parsing. In: Proceedings of the Conference on Computational Natural Language Learning, CoNLL–X (2006)
Wu, Y., Lee, Y., Yang, J.: The Exploration of Deterministic and Efficient Dependency Parsing. In: Proceedings of the Conference on Computational Natural Language Learning, CoNLL–X (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ballesteros, M., Herrera, J., Francisco, V., Gervás, P. (2010). A Feasibility Study on Low Level Techniques for Improving Parsing Accuracy for Spanish Using Maltparser. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2010. Lecture Notes in Computer Science(), vol 6040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12842-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-12842-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12841-7
Online ISBN: 978-3-642-12842-4
eBook Packages: Computer ScienceComputer Science (R0)