Abstract
An important reason to prefer dependency parsing over classical phrased based methods, especially for languages such as Persian, with the property of being “free word order”, is that this particular property has a negative impact on the accuracy of conventional parsing methods. In Persian, some words such as adverbs can freely be moved within a sentence without affecting its correctness or meaning. In this paper, we illustrate the robustness of dependency parsing against this particular problem by training two well-known dependency parsers, namely MST Parser and Malt Parser, using a Persian dependency corpus called Dadegan. We divided the corpus into two separate parts including only projective sentences and only non-projective sentences, which are corelated with the free word order property. As our results show, MST Parsing is not only more tolerant than Malt Parsing against the free word order problem, but it is also in general a more accurate technique.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agirre, E., Kepa, B., Koldo, G., Nivre, J.: Improving dependency parsing with semantic classes. In: Association for Computational Linguistics: Human Language Technologies: short papers, pp 699–703 (2011)
Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning, pp. 273–297 (1995)
Heshaam, F., Ghassem-Sani, G.: Unsupervised grammar induction using history based approach. In: Computer Speech & Language, pp. 644–658 (2006)
Klein, D., Manning, C.D.: Natural language grammar induction using a constituent-context model. In: Advances in Neural Information Processing Systems, pp. 35–42 (2001)
Klein, D., Manning, C.D.: Corpus-based induction of syntactic structure: Models of dependency and constituency. In: Association for Computational Linguistics (2004)
Koo, T., Carreras, X., Collins, M.: Simple semi-supervised dependency parsing. In: ACL, pp. 595–603 (2008)
McDonald, R., Pereira, F., Ribarov, K., Hajic, J.: Non-projective dependency parsing using spanning tree algorithms. In: Human Language Technology and Empirical Methods in Natural Language Processing (HLT/EMNLP), pp. 523–530 (2005)
Mesgar, M., Ghasem-Sani, G.: History Based Unsupervised Data Oriented Parsing. In: RANLP, pp. 453–459 (2013)
Mirroshandel, S.A., Ghassem-Sani, G.: Unsupervised Grammar Induction Using a Parent Based Constituent Context Model. In: ECAI, pp. 293–297 (2008)
Nivre, J.: Dependency grammar and dependency parsing. Technical Report MSI (2005)
Nivre, J., Hall, J., Nilsson, J.: Maltparser: A data-driven parser-generator for dependency parsing. In: Language Resources and Evaluation (LREC), Genoa, Italy, pp. 2216–2219 (2006)
Rasooli, M.S., Kouhestani, M., Moloodi, A.: Development of a Persian Syntactic Dependency Treebank. In: North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 306–314 (2013)
Seraji, M., Beatam, M., Nivre, J.: Dependency parsers for Persian. In: Asian Language Resources, COLING, pp. 35–43 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Falavarjani, S.A.M., Ghassem-Sani, G. (2015). Advantages of Dependency Parsing for Free Word Order Natural Languages. In: Italiano, G.F., Margaria-Steffen, T., Pokorný, J., Quisquater, JJ., Wattenhofer, R. (eds) SOFSEM 2015: Theory and Practice of Computer Science. SOFSEM 2015. Lecture Notes in Computer Science, vol 8939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46078-8_42
Download citation
DOI: https://doi.org/10.1007/978-3-662-46078-8_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46077-1
Online ISBN: 978-3-662-46078-8
eBook Packages: Computer ScienceComputer Science (R0)