Time-sensitive Arabic multiword expressions extraction from social networks

• Al-Haj, H. (2010). Identifying multi-word expressions by leveraging morphological and syntactic idiosyncrasy. In Proceedings of the 23rd international conference on computational linguistics (COLING).

• Alkouz, A. & Albayrak, S. (2012). An interests discovery approach in social networks based on semantically enriched graphs. In International conference on advances in social networks analysis and mining, Istanbul.

• Baldwin, T. et al. (2008). A machine learning approach to multiword expression extraction. In Proceedings of the LREC workshop towards a shared task for multiword expressions.

• Bar, K. & Dershowitz N. (2014). Inferring paraphrases for a highly inflected language from a monolingual corpus. In Computational linguistics and intelligent text processing, Lecture notes in computer science, New York: Springer, 8404, pp 254–270.

• Bruce, C., et al. (2009). Search engines: Information retrieval in practice. Boston: Addison-Wesley Publishing Company.

  Google Scholar 

• Covington, M. A. (1992). A dependency parser for variable-word-order languages. In K. R. Billingsley, H. U. Brown III, & E. Derohanes (Eds.), Computer assisted modeling on the IBM 3090: Papers from the 1989 IBM supercomputing competition. Athens: Baldwin Press.

  Google Scholar 

• Daoud, D. (2005). Arabic Deconversion in the framework of the universal networking language. In J. Cardeٌosa, A. Gelbukh & E. Tovar (Eds.), Universal networking language, Advances in Theory and Applications. Research on Computing Science (Vol. 12).

• Daoud, D. & Qais H. (2011). Stemming arabic using longest-match and dynamic normalization. In Arabic language technology international conference (ALTIC) 2011, Bibliotheca Alexandrina (B.A.), Alexandria.

• Daoud, D., & Boitet, C. (2014). Correctness, strength and similarity evaluation of stemming algorithms for arabic. The Egyptian Journal of Language Engineering, 1(1), 17–23.

  Google Scholar 

• Daoud, D., et al. (2015). Arabic tweets clustering and labeling based on lingual and semantically enriched bayesian network model. Recent Patents on Computer Science, 8(2), 1–14.

  Article  MathSciNet  Google Scholar 

• Ethnologue (2015). Ethnologue languages of the world. Retrieved 2015, from http://www.ethnologue.com/statistics/size.

• Frank, S. (1993). Retrieving collocations from text: Xtract. Computational Linguistics, 19(1), 143–177.

  Google Scholar 

• Graham, K. & Giesbrecht, E. (2006). Automatic identification of non-compositional multiword expressions using latent semantic analysis. In Workshop on multiword expressions: Identifying and exploiting underlying properties, Sydney: Association for Computational Linguistics.

• Grinev, M. et al. (2011). Analytics for the realtime web. In Proceedings of the VLDB endowment.

• Haewoon, K., et al. (2010). What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web. Raleigh: ACM.

• Ivan, A. S. et al. (2002). Multiword expressions: a pain in the neck for NLP. In Proceedings of the third international conference on computational linguistics and intelligent text processing, Springer-Verlag.

• Jackendoff, R. (1997). The architecture of the language faculty. Cambridge, MA: MIT Press.

  Google Scholar 

• Kenneth Ward, C., & Patrick, H. (1990). Word association norms, mutual information, and lexicography. Computational Linguistic, 16(1), 22–29.

  Google Scholar 

• Meghdad, F. & Ronaldo M. (2014). A supervised model for extraction of multiword expressions, based on statistical context features. In Proceedings of the 10th workshop on multiword expressions (MWE), Gothenburg: Association for Computational Linguistics.

• Piao, S. S., et al. (2005). Comparing and combining a semantic tagger and a statistical tool for MWE extraction. Computer Speech & Language, 19(4), 378.

  Article  Google Scholar 

• Ramisch, C. (2015). Multiword expressions acquisition: A generic and open framework. Cham: Springer.

  Book  Google Scholar 

• Ramisch, C. et al. (2010). Multiword Expressions in the wild? The mwetoolkit comes in handy. COLING (Demos), In Demonstrations volume.

• Salloum, W. & Habash N. (2011). Dialectal to standard Arabic paraphrasing to improve Arabic-English statistical machine translation. In Dialects workshop at the conference for empirical methods in natural language processing, Edinburgh.

• Uherčík, T. et al. (2013). Utilizing microblogs for web page relevant term acquisition. In SOFSEM 2013: Theory and practice of computer science lecture notes in computer science, 7741: pp. 457–468.

• Veronika Vincze, N. T. I. & Berend G. (2011). Multiword expressions and named entities in the Wiki50 corpus. In International conference recent advances in natural language processing, RANLP.

• Yassin, Y. A. (2003). Why arabic is the most difficult language for localization. Globalization Insider, XII(3.6), 5.

  Google Scholar 

• Yulia, T. & Shuly W. (2010). Extraction of multi-word expressions from small parallel corpora. In Proceedings of the 23rd international conference on computational linguistics: Posters, Beijing: Association for Computational Linguistics.

• Yulia, T., & Shuly, W. (2014). Identification of multiword expressions by combining multiple linguistic information sources. Computational Linguistics, 40, 449–468.

  Article  Google Scholar 

Download references