Abstract
A lot of work has been done to give the individual words of a certain language adequate representations in vector space so that these representations capture semantic and syntactic properties of the language. In this paper, we compare different techniques to build vectorized space representations for Arabic, and test these models via intrinsic and extrinsic evaluations. Intrinsic evaluation assesses the quality of models using benchmark semantic and syntactic dataset, while extrinsic evaluation assesses the quality of models by their impact on two Natural Language Processing applications: Information retrieval and Short Answer Grading. Finally, we map the Arabic vector space to the English counterpart using Cosine error regression neural network and show that it outperforms standard mean square error regression neural networks in this task.
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References
Collobert, R., Weston, J.: A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. In: Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, pp. 160–167 (2008)
Mnih, A., Hinton, G.: A Scalable Hierarchical Distributed Language Model. In: NIPS: Proceedings of Neural Information Processing Systems, Vancouver, B.C, Canada, pp. 1081–1088 (2009)
Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: NAACL-HLT: Proceedings of North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751 (2013)
Turian, J., Ratinov, L., Bengio, Y.: Word representations: A simple and general method for semi-supervised learning. In: ACL: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394 (2010)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. In: ICLR: Proceeding of the International Conference on Learning Representations Workshop Track, Arizona, USA, pp. 1301–3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed Representation of Words and Phrases and their Compositionality. In: NIPS: Proceedings of Neural Information Processing Systems Nevada, United States, pp. 3111–3119 (2013)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: EMNLP: Proceeding of the Empirical Methods in Natural Language Processing, Doha, Qatar, pp. 1532–1543 (2014)
http://opus.lingfil.uu.se/ (accessed January 29, 2015)
Tiedemann, J.: Parallel Data, Tools and Interfaces in OPUS. In: LREC: Proceedings of the 8th International Conference on Language Resources and Evaluation, Istanbul, Turkey, pp. 2214–2218 (2012)
Raafat, H., Zahran, M., Rashwan, M.: Arabase A Database Combining Different Arabic Resources with Lexical and Semantic Information. In: Proceeding of KDIR is part of IC3K, The International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Portugal, pp. 233–240 (2013)
Eisele, A., Chen, Y.: MultiUN: A Multilingual corpus from United Nation Documents. In: LREC: Proceeding of the International Conference on Language Resources and Evaluation, Valletta, Malta, pp. 17–23 (2010)
http://www.opensubtitles.org/ (accessed January 29, 2015)
http://tanzil.net/download/ (accessed January 29, 2015)
Tiedemann, J.: News from OPUS - A Collection of Multilingual Parallel Corpora with Tools and Interfaces. In (RANLP): Recent Advances in Natural Language Processing, pp. 237–248. John Benjamins, Amsterdam (2009)
https://sites.google.com/site/mouradabbas9/corpora (accessed January 29, 2015)
Saad, M.K., Ashour, W.: OSAC: Open Source Arabic Corpus. In: EEECS: the 6th International Symposium on Electrical and Electronics Engineering and Computer Science, European University of Lefke, Cyprus, vol. 10 (2010)
https://github.com/anastaw/Meedan-Memory (accessed January 29, 2015)
http://ksucorpus.ksu.edu.sa/ar/ (accessed January 29, 2015)
https://code.google.com/p/word2vec/ (accessed January 29, 2015)
http://nlp.stanford.edu/projects/glove/ (accessed January 29, 2015)
Mikolov, T., Le, V.Q., Sutskever, I.: Exploiting Similarities among Languages for Machine Translation. In: arXiv, 1309-4168 (2013)
Gomaa, W.H., Fahmy, A.A.: Automatic scoring for answers to Arabic test questions. Computer Speech & Language, 833–857 (2014)
Mahgoub, Y.A., Rashwan, A.M., Raafat, H., Zahran, A.M., Fayek, B.M.: Semantic Query Expansion for Arabic Information Retrieval. In: EMNLP: The Arabic Natural Language Processing Workshop, Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp. 87–92 (2014)
Oard, D.W., Gey, F.C.: The TREC 2002 Arabic/English CLIR Track. In: TREC (2002)
http://sourceforge.net/p/lemur/wiki/Indri/ (accessed January 31, 2015)
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Zahran, M.A., Magooda, A., Mahgoub, A.Y., Raafat, H., Rashwan, M., Atyia, A. (2015). Word Representations in Vector Space and their Applications for Arabic. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9041. Springer, Cham. https://doi.org/10.1007/978-3-319-18111-0_32
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DOI: https://doi.org/10.1007/978-3-319-18111-0_32
Publisher Name: Springer, Cham
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