Using Deep Neural Networks for Extracting Sentiment Targets in Arabic Tweets

Author:

El-Kilany Ayman,Azzam Amr,El-Beltagy Samhaa R.

Publisher

Springer International Publishing

Reference19 articles.

1. Zayed, O., El-Beltagy, S.R.: Named entity recognition of persons’ names in Arabic tweets. In: Proceedings of Recent Advances in Natural Language Processing (RANLP 2015), Hissar, Bulgaria (2015)

2. Zayed, O., El-Beltagy, S.R.: A hybrid approach for extracting arabic persons’ names and resolving their ambiguity from twitter. In: Métais, E. et al. (eds.) Proceedings of 19th International Conference on Application of Natural Language to Information Systems (NLDB2015), NLDB 2015, Lecture Notes in Computer Science (LNCS), Passau, Germany (2015)

3. El-Beltagy, S.R., Khalil, T., Halaby, A., Hammad, M.H.: Combining lexical features and a supervised learning approach for Arabic sentiment analysis. In: CICLing 2016, Konya, Turkey (2016)

4. Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv:1508.01991 (2015)

5. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML, pp. 282–289 (2001)

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