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A Hybrid Approach to Parsing Natural Languages

  • Sardar JafEmail author
  • Allan Ramsay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9561)

Abstract

Ambiguities in natural languages make processing (parsing) them a difficult task. Parsing is even more difficult when dealing with a structurally complex natural language such as Arabic. In this paper, we briefly highlight some of the complex structure of Arabic, and we identify different parsing approaches and briefly discuss their limitations. Our goal is to produce a hybrid parser, by combining different parsing approaches, which retains the advantages of data-driven approaches but is guided by a set of grammatical rules to produce more accurate results. We describe a novel technique for directly combining different parsing approaches. Results for our initial experiments that we have conducted in this work, and our plans for future work are also presented.

Keywords

Parsing Hybrid parsing Natural language processing Dependency parsing 

Notes

Acknowledgments

Sardar Jaf’s contribution to this work was supported by the Qatar National Research Fund (grant NPRP 09-046-6-001). Allan Ramsay’s contribution was partially supported from the same grant.

References

  1. 1.
    Aho, A.V., Ullman, J.D.: The Theory of Parsing, Translation, and Compiling, vol. 1. Prentice-Hall, Englewood Cliffs (1972)zbMATHGoogle Scholar
  2. 2.
    Farghaly, A., Shaalan, K.: Arabic natural language processing: challenges and solutions. ACM Comput. Surveys 8(4), 1–22 (2009)Google Scholar
  3. 3.
    Collins, M.: Head-driven statistical models for natural language parsing. Comput. Linguist. 29(4), 589–637 (2003). http://dx.doi.org/10.1162/089120103322753356 MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Lee, C., Day, M., Sung, C., Lee, Y., Jiang, T., Wu, C., Shih, C., Chen, Y., Hsu, W.: Boosting Chinese question answering with two lightweight methods: ABSPs and SCO-QAT. ACM Trans. Asian Lang. Inf. Process. (TALIP) 7(4), 12:1–12:29 (2008)Google Scholar
  5. 5.
    Nivre, J., Hall, J., Nilsson, J., Eryigit, G., Svetoslav, M.: Labeled pseudo-projective dependency parsing with support vector machines, pp. 221–225. Association for Computational Linguistics (2006)Google Scholar
  6. 6.
    Nivre, J.: Inductive Dependency Parsing. Text, Speech and Language Technology. Springer, Netherlands (2006)CrossRefzbMATHGoogle Scholar
  7. 7.
    Kaplan, R.M., Riezler, S., King, T.H., Maxwell Iii, J.T., Vasserman, E., Crouch, R.: Speed and accuracy in shallow and deep stochastic parsing. In: Proceedings of Human Langauge Technology and the Conference of the North American Chapter of the Association for Computational Linguistics, HLT-NAACL, pp. 97–104 (2004)Google Scholar
  8. 8.
    Baptista, M.: On the nature of pro-drop in capeverdean creole. Harv. Working Pap. Linguist. 5, 3–17 (1995)Google Scholar
  9. 9.
    Daimi, K.: Identifying syntactic ambiguities in single-parse Arabic sentence. Comput. Humanit. 35(3), 333–349 (2001)CrossRefGoogle Scholar
  10. 10.
    Attia, A.M.: Handling Arabic morphological and syntactic ambiguities within the LFG framework with a view to machine translation. Ph.D. Thesis, School of Languages, Linguistics and Cultures, Manchester University (2008)Google Scholar
  11. 11.
    Ramsay, A., Mansour, H.: Local constraints on Arabic word order. In: Salakoski, T., Ginter, F., Pyysalo, S., Pahikkala, T. (eds.) FinTAL 2006. LNCS (LNAI), vol. 4139, pp. 447–457. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Nelken, R., Shieber, S.M.: Arabic diacritization using weighted finite-state transducers. In: Proceedings of the Association for Computational Linguistics Workshop on Computational Approaches to Semitic Languages. Semitic 2005, pp. 79–86. Association for Computational Linguistics, Stroudsburg (2005)Google Scholar
  13. 13.
    Nivre, J., Hall, J., Nilsson, J.: MaltParser: A Data-Driven Parser-Generator for Dependency Parsing. Springer, Netherland (2006)Google Scholar
  14. 14.
    MacDonald, R.: Discriminative learning and spanning tree algorithms for dependency parsing. Ph.D. Thesis, Computer and Information Science, the University of Pennsylvania (2006). http://www.cis.upenn.edu/grad/documents/mcdonald.pdf
  15. 15.
    Øvrelid, L., Kuhn, J., Spreyer, K.: Improving data-driven dependency parsing using large-scale LFG grammars. In: Proceedings of the Association for Computational Linguistics-International Joint Conference on Natural Language Processing 2009 Conference Short Papers, pp. 37–40. Association for Computational Linguistics, Stroudsburg (2009). http://dl.acm.org/citation.cfm?id=1667583.1667597
  16. 16.
    Sagae, K., Miyao, Y.: HPSG parsing with shallow dependency constraints. In: Proceedings of ACL 2007 (2007)Google Scholar
  17. 17.
    Marton, Y., Habash, N., Rambow, O.: Improving Arabic dependency parsing with form-based and functional morphological features. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 1586–1596. Association for Computational Linguistics, Portland (2011). http://www.aclweb.org/anthology/P11-1159
  18. 18.
    Maamouri, M., Bies, A.: Developing an Arabic treebank: methods, guidelines, procedures, and tools. In: Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages, Geneva, pp. 2–9 (2004)Google Scholar
  19. 19.
    McDonald, R., Lerman, K., Pereira, F.: Multilingual dependency parsing with a two-stage discriminative parser. In: Tenth Conference on Computational Natural Language Learning (CoNLL-X), New York (2006)Google Scholar
  20. 20.
    Xia, F., Palmer, M.: Converting dependency structures to phrase structures. In: Proceedings of the 1st Human Language Technology Conference (HLT-2001), San Diego, pp. 1–5 (2001)Google Scholar
  21. 21.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Series in Data Management Systems, 2nd edn. Morgan Kaufmann, San Francisco (2005). http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0120884070
  22. 22.
    Ramsay, A.M.: Direct parsing with discontinuous phrases. Nat. Lang. Eng. 5(3), 271–300 (1999)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computer ScienceThe University of ManchesterManchesterUK

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