Abstract
In this paper, we propose a set of language resources for building Turkish language processing applications. Specifically, we present a finite-state implementation of a morphological parser, an averaged perceptron-based morphological disambiguator, and compilation of a web corpus. Turkish is an agglutinative language with a highly productive inflectional and derivational morphology. We present an implementation of a morphological parser based on two-level morphology. This parser is one of the most complete parsers for Turkish and it runs independent of any other external system such as PC-KIMMO in contrast to existing parsers. Due to complex phonology and morphology of Turkish, parsing introduces some ambiguous parses. We developed a morphological disambiguator with accuracy of about 98% using averaged perceptron algorithm. We also present our efforts to build a Turkish web corpus of about 423 million words.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Collins, M.: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms. In: EMNLP (2002)
Dilek, Z.H.T., Oflazer, K., Tür, G.: Statistical Morphological Disambiguation for Agglutinative Languages. Computers and the Humanities 36(4) (2002)
Ezeiza, N., Alegria, I., Arriola, J.M., Urizar, R., Aduriz, I.: Combining Stochastic and Rule-based Methods for Disambiguation in Agglutinative Languages. In: COLING-ACL (1998)
Hajic, J., Hladka, B.: Tagging Inflective Languages: Prediction of Morphological Categories for a Rich, Structured Tagset. In: COLING-ACL, pp. 483–490 (1998)
Koskenniemi, K.: A General Computational Model for Word-form Recognition and Production. In: 22nd Annual Meeting on Association for Computational Linguistics, pp. 178–181 (1984)
Lewis, G.: Turkish Grammar. Oxford University Press, Oxford (2001)
Megyesi, B.: Improving Brill’s PoS Tagger for an Agglutinative Language. In: Joint Sigdat Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (1999)
Mohri, M.: Finite-state Transducers in Language and Speech Processing. Computational Linguistics 23(2), 269–311 (1997)
Oflazer, K., Tür, G.: Morphological Disambiguation by Voting Constraints. In: ACL, pp. 222–229 (1997)
Sak, H., Güngör, T., Saraçlar, M.: Morphological Disambiguation of Turkish Text with Perceptron Algorithm. In: Gelbukh, A. (ed.) CICLing 2007. LNCS, vol. 4394, pp. 107–118. Springer, Heidelberg (2007)
Salor, Ö., Pellom, B.L., Çiloğlu, T., Hacıoğlu, K., Demirekler, M.: On Developing New Text and Audio Corpora and Speech Recognition Tools for the Turkish Language. In: ICSLP (2002)
Say, B., Zeyrek, D., Oflazer, K., Özge, U.: Development of a Corpus and a Treebank for Present-day Written Turkish. In: 11th International Conference of Turkish Linguistics (2002)
Yüret, D., Türe, F.: Learning Morphological Disambiguation Rules for Turkish. In: HLT-NAACL (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sak, H., Güngör, T., Saraçlar, M. (2008). Turkish Language Resources: Morphological Parser, Morphological Disambiguator and Web Corpus. In: Nordström, B., Ranta, A. (eds) Advances in Natural Language Processing. GoTAL 2008. Lecture Notes in Computer Science(), vol 5221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85287-2_40
Download citation
DOI: https://doi.org/10.1007/978-3-540-85287-2_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85286-5
Online ISBN: 978-3-540-85287-2
eBook Packages: Computer ScienceComputer Science (R0)