W
Wael Hassan Gomaa
Researcher at Beni-Suef University
Publications - 15
Citations - 917
Wael Hassan Gomaa is an academic researcher from Beni-Suef University. The author has contributed to research in topics: Semantic similarity & Document clustering. The author has an hindex of 7, co-authored 11 publications receiving 727 citations. Previous affiliations of Wael Hassan Gomaa include Modern Academy In Maadi.
Papers
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Journal ArticleDOI
A Survey of Text Similarity Approaches
Wael Hassan Gomaa,Aly A. Fahmy +1 more
TL;DR: This survey discusses the existing works on text similarity through partitioning them into three approaches; String-based, Corpus-based and Knowledge-based similarities, and samples of combination between these similarities are presented.
Journal ArticleDOI
Short Answer Grading Using String Similarity And Corpus-Based Similarity
Wael Hassan Gomaa,Aly A. Fahmy +1 more
TL;DR: This paper presents a different unsupervised approach which deals with students’ answers holistically using text to text similarity using Bag of Words (BOW) when compared to previous work.
Journal ArticleDOI
Automatic scoring for answers to Arabic test questions
Wael Hassan Gomaa,Aly A. Fahmy +1 more
TL;DR: This research presents the first benchmark Arabic data set that contains 610 students’ short answers together with their English translations, and focuses on applying multiple similarity measures separately and in combination.
Journal ArticleDOI
Credibility Detection in Twitter Using Word N-gram Analysis and Supervised Machine Learning Techniques
TL;DR: A classification model based on supervised machine learning techniques and word-based N-gram analysis to classify Twitter messages automatically into credible and not credible and experiments show that the proposed model achieved an improvement when compared to two models existing in the literature.
Book ChapterDOI
Ans2vec: A Scoring System for Short Answers
Wael Hassan Gomaa,Aly A. Fahmy +1 more
TL;DR: An efficient and uncomplicated short answer grading model named Ans2vec is proposed, used to convert both model and student’s answers into meaningful vectors to measure the similarity between them.