Ashraf Mahgoub


SimVecs: Similarity-Based Vectors for Utterance Representation in Conversational AI Systems
Ashraf Mahgoub | Youssef Shahin | Riham Mansour | Saurabh Bagchi
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Conversational AI systems are gaining a lot of attention recently in both industrial and scientific domains, providing a natural way of interaction between customers and adaptive intelligent systems. A key requirement in these systems is the ability to understand the user’s intent and provide adequate responses to them. One of the greatest challenges of language understanding (LU) services is efficient utterance (sentence) representation in vector space, which is an essential step for most ML tasks. In this paper, we propose a novel approach for generating vector space representations of utterances using pair-wise similarity metrics. The proposed approach uses only a few corpora to tune the weights of the similarity metric without relying on external general purpose ontologies. Our experiments confirm that the generated vectors can improve the performance of LU services in unsupervised, semi-supervised and supervised learning tasks.


RDI_Team at SemEval-2016 Task 3: RDI Unsupervised Framework for Text Ranking
Ahmed Magooda | Amr Gomaa | Ashraf Mahgoub | Hany Ahmed | Mohsen Rashwan | Hazem Raafat | Eslam Kamal | Ahmad Al Sallab
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


Semantic Query Expansion for Arabic Information Retrieval
Ashraf Mahgoub | Mohsen Rashwan | Hazem Raafat | Mohamed Zahran | Magda Fayek
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)