Ayman Alhelbawy


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The QMUL/HRBDT contribution to the NADI Arabic Dialect Identification Shared Task
Abdulrahman Aloraini | Massimo Poesio | Ayman Alhelbawy
Proceedings of the Fifth Arabic Natural Language Processing Workshop

We present the Arabic dialect identification system that we used for the country-level subtask of the NADI challenge. Our model consists of three components: BiLSTM-CNN, character-level TF-IDF, and topic modeling features. We represent each tweet using these features and feed them into a deep neural network. We then add an effective heuristic that improves the overall performance. We achieved an F1-Macro score of 20.77% and an accuracy of 34.32% on the test set. The model was also evaluated on the Arabic Online Commentary dataset, achieving results better than the state-of-the-art.


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Towards a Corpus of Violence Acts in Arabic Social Media
Ayman Alhelbawy | Poesio Massimo | Udo Kruschwitz
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper we present a new corpus of Arabic tweets that mention some form of violent event, developed to support the automatic identification of Human Rights Abuse. The dataset was manually labelled for seven classes of violence using crowdsourcing.


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Graph Ranking for Collective Named Entity Disambiguation
Ayman Alhelbawy | Robert Gaizauskas
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Collective Named Entity Disambiguation using Graph Ranking and Clique Partitioning Approaches
Ayman Alhelbawy | Robert Gaizauskas
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers