Abstract
Building a system to extract Arabic named entities is a complex task due to the ambiguity and structure of Arabic text. Previous approaches that have tackled the problem of Arabic named entity recognition relied heavily on Arabic parsers and taggers combined with a huge set of gazetteers and sometimes large training sets to solve the ambiguity problem. But while these approaches are applicable to modern standard Arabic (MSA) text, they cannot handle colloquial Arabic. With the rapid increase in online social media usage by Arabic speakers, it is important to build an Arabic named entity recognition system that deals with both colloquial Arabic and MSA text. This paper introduces an approach for extracting Arabic persons’ name without utilizing any Arabic parsers or taggers. Evaluation of the presented approach shows that it achieves high precision and an acceptable level of recall on a benchmark dataset.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Abdallah, S., Shaalan, K., Shoaib, M.: Integrating rule-based system with classification for Arabic named entity recognition. In: Gelbukh, A. (ed.) CICLing 2012, Part I. LNCS, vol. 7181, pp. 311–322. Springer, Heidelberg (2012)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD 1993, New York, pp. 207–216 (1993)
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)
Benajiba, Y., Diab, M., Rosso, P.: Arabic named entity recognition using optimized feature sets. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, pp. 284–293. Association for Computational Linguistics, Morristown (2008)
Benajiba, Y., Diab, M., Rosso, P.: Arabic named entity recognition: A feature-driven study. IEEE Transactions on Audio, Speech, and Language Processing 17(5), 926–934 (2009)
Benajiba, Y., Diab, M., Rosso, P.: Arabic named entity recognition: An svm-based approach. In: The International Arab Conference on Information Technology, ACIT 2008 (2008)
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)
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)
Blondel, V.D., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10008 (2008)
Elsebai, A., Meziane, F., Belkredim, F.Z.: A rule based persons names Arabic extraction system. In: The 11th International Business Information Management Association Conference, IBIMA 2009, Cairo, pp. 1205–1211 (2009)
Farghaly, A., Shaalan, K.: Arabic natural language processing: Challenges and solutions. ACM Transactions on Asian Language Information Processing 8(4), 1–22 (2009)
Larkey, L., Ballesteros, L., Connell, M.E.: Light stemming for Arabic information retrieval. Arabic Computational Morphology 38, 221–243 (2007)
Mansouri, A., Affendey, L.S., Mamat, A.: Named entity recognition using a new fuzzy support vector machine. In: Proceedings of the 2008 International Conference on Computer Science and Information Technology, ICCSIT 2008, Singapore, pp. 24–28 (2008)
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, India, pp. 2159–2176 (2012)
Shaalan, K., Raza, H.: NERA: Named entity recognition for Arabic. Journal of the American Society for Information Science and Technology, 1652–1663 (2009)
Traboulsi, H.: Arabic named entity extraction: A local grammar-based approach. In: Proceedings of the International Multiconference on Computer Science and Information Technology, vol. 4, pp. 139–143 (2009)
Zayed, O., El-Beltagy, S., Haggag, O.: A novel approach for detecting Arabic persons’ names using limited resources. In: Complementary Proceedings of 14th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2013, Greece (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zayed, O., El-Beltagy, S., Haggag, O. (2013). An Approach for Extracting and Disambiguating Arabic Persons’ Names Using Clustered Dictionaries and Scored Patterns. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2013. Lecture Notes in Computer Science, vol 7934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38824-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-38824-8_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38823-1
Online ISBN: 978-3-642-38824-8
eBook Packages: Computer ScienceComputer Science (R0)