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Hybrid Feature Selection Approach for Arabic Named Entity Recognition

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Book cover Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9623))

Abstract

Named Entity Recognition (NER) task has drawn a great attention in the research field in the last decade, as it played an important role in the Natural Language Processing (NLP) applications; In this paper, we investigate the effectiveness of a hybrid feature subset selection approach for Arabic Named Entity Recognition (NER) which is presented using filtering approach and optimized Genetic algorithm. Genetic algorithm is utilized through parallelization of the fitness computation in order to reduce the computation time to search out the most appropriate and informative combination of features for classification. Support Vector Machine (SVM) is used as the machine learning based classifier to evaluate the accuracy of the Arabic NER through the proposed approach. ANER is the dataset used in our experiments which is presented by both language independent and language specific features in Arabic NER; Experimental results show the effectiveness of the feature subsets obtained by the proposed hybrid approach which are smaller and effective than the original feature set that leads to a considerable increase in the classification accuracy.

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References

  1. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes. 30(1), 3–26 (2007)

    Article  Google Scholar 

  2. Benajiba, Y., Diab, M., Rosso, P.: Arabic named entity recognition: a feature-driven study. IEEE Trans. Audio Speech Lang. Process. 17(5), 926–934 (2009)

    Google Scholar 

  3. Abouenour, L., Bouzoubaa, K., Rosso, P.: IDRAAQ: new Arabic question answering system based on query expansion and passage retrieval. CLEF (Online Working Notes/Labs/Workshop) (2012)

    Google Scholar 

  4. Babych, B., Hartley, A.: Improving machine translation quality with automatic named entity recognition. In: Proceedings of the 7th International EAMT workshop on MT and other Language Technology Tools, Improving MT through other Language Technology Tools: Resources and Tools for Building MT, pp. 1–8. Association for Computational Linguistics (2003)

    Google Scholar 

  5. Zaghouani, W.: RENAR: A rule-based Arabic named entity recognition system. ACM Trans. Asian Lang. Inf. Process. (TALIP) 11(1), 2 (2012)

    MathSciNet  Google Scholar 

  6. Shaalan, K.: Rule-based approach in Arabic natural language processing. Int. J. Inf. Commun. Technol. (IJICT) 3(3), 11–19 (2010)

    Google Scholar 

  7. Shaalan, K., Raza, H.: NERA: named entity recognition for arabic. J. Am. Soc. Inf. Sci. Technol. 60(8), 1652–1663 (2009)

    Google Scholar 

  8. Oudah, M., Shaalan, K.: A pipeline Arabic named entity recognition using a hybrid approach. In: Proceedings of the 24th International Conference on Computational Linguistics, COLING 2012, pp. 2159–2176. India (2012)

    Google Scholar 

  9. Meselhi, M.A., Bakr, H.M.A., Ziedan, I., Shaalan, K.: A novel hybrid approach to arabic named entity recognition. In: Shi, X., Chen, Y. (eds.) CWMT 2014. CCIS, vol. 493, pp. 93–103. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45701-6_9

    Google Scholar 

  10. Benajiba, Y., Rosso, P., BenedíRuiz, J.M.: ANERsys: an arabic named entity recognition system based on maximum entropy. In: Gelbukh, A. (ed.) CICLing 2007. LNCS, vol. 4394, pp. 143–153. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-70939-8_13

    Google Scholar 

  11. Mohit, B., Schneider, N., Bhowmick, R., Oflazer, K., Smith, N.A.: Recall-oriented learning of named entities in Arabic Wikipedia. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 162–173. Association for Computational Linguistics (2012)

    Google Scholar 

  12. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997)

    Google Scholar 

  13. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    Google Scholar 

  14. Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning, pp. 147–155. Association for Computational Linguistics (2009)

    Google Scholar 

  15. Pahikkala, T., Airola, A., Salakoski, T.: Speeding up greedy forward selection for regularized least-squares. In: Proceedings of the Ninth International Conference on Machine Learning and Applications, ICMLA 2010, pp. 325–330 (2010)

    Google Scholar 

  16. Kabir, M.M., Shahjahan, M., Murase, K..: A backward feature selection by creating compact neural network using coherence learning and pruning. In: Proceedings of JACIII 11, pp. 570–581 (2007)

    Google Scholar 

  17. Benajiba, Y., Rosso, P.: Anersys 2.0: conquering the ner task for the Arabic language by combining the maximum entropy with pos-tag information. In: IICAI, pp. 1814–1823 (2007)

    Google Scholar 

  18. Benajiba, Y., Rosso, P.: Arabic named entity recognition using conditional random fields. In: Workshop on HLT & NLP within the Arabic World. Arabic Language and Local Languages Processing: Status Updates and Prospects (2008)

    Google Scholar 

  19. Benajiba, Y., Diab, M.T., Rosso, P.: Using language independent and language specific features to enhance Arabic named entity recognition. Int. Arab J. Inf. Technol. 6(5), 463–471 (2009)

    Google Scholar 

  20. Abdul-Hamid, A., Darwish, K.: Simplified feature set for Arabic named entity recognition. In: Proceedings of the 2010 Named Entities Workshop (NEWS 2010), pp. 110–115, Stroudsburg, PA, USA. Association for Computational Linguistics (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Arabic Academy for Science, Technology and Maritime Transport, Cairo, Egypt

    Miran Shahine

  2. Shorouk Academy, Cairo, Egypt

    Mohamed Sakre

Authors
  1. Miran Shahine
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  2. Mohamed Sakre
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Corresponding author

Correspondence to Miran Shahine .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Shahine, M., Sakre, M. (2018). Hybrid Feature Selection Approach for Arabic Named Entity Recognition. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_32

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  • DOI: https://doi.org/10.1007/978-3-319-75477-2_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75476-5

  • Online ISBN: 978-3-319-75477-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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