Mohamed El Bachir Menai


2019

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KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation
Nourah Alswaidan | Mohamed El Bachir Menai
Proceedings of the 13th International Workshop on Semantic Evaluation

We proposed a model to address emotion recognition in textual conversation based on using automatically extracted features and human engineered features. The proposed model utilizes a fast gated-recurrent-unit backed by CuDNN, and a convolutional neural network to automatically extract features. The human engineered features take the frequency-inverse document frequency of semantic meaning and mood tags extracted from SinticNet.

2018

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Ant Colony System for Multi-Document Summarization
Asma Al-Saleh | Mohamed El Bachir Menai
Proceedings of the 27th International Conference on Computational Linguistics

This paper proposes an extractive multi-document summarization approach based on an ant colony system to optimize the information coverage of summary sentences. The implemented system was evaluated on both English and Arabic versions of the corpus of the Text Analysis Conference 2011 MultiLing Pilot by using ROUGE metrics. The evaluation results are promising in comparison to those of the participating systems. Indeed, our system achieved the best scores based on several ROUGE metrics.