Browsing by Author "Gomaa, Wael H"

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Empowering MBTI Personality Classification through Transformer-Based Summarization Model
(IEEE, 2023-07) Elmoushy, Seif; Saeed, Mostafa; Gomaa, Wael H
The Myers-Briggs Type Indicator (MBTI) is a popular personality classification tool that utilizes the work of Carl Jung. With individuals increasingly expressing themselves online rather than in person, social media has become a promising platform for predicting personality. However, predicting personality from online behavior is a challenging task that requires extensive data processing and modeling. To tackle this challenge, a novel approach is proposed that leverages a transformer-based summarization model to summarize the dataset records before applying the DistilBERT base classification model. The proposed method improves the MBTI classification task by achieving an accuracy rate of 0.96, which demonstrates the efficacy and durability of the strategy. The study emphasizes the importance of transformer-based summarization in enhancing NLP tasks and the need for applying various optimization techniques to achieve optimal performance. The findings provide a foundation for future research in personality classification and NLP.
Empowering Short Answer Grading: Integrating Transformer-Based Embeddings and BI-LSTM Network
(MDPI AG, 2023-06) Gomaa, Wael H; Nagib, Abdelrahman E; Saeed, Mostafa M; Algarni, Abdulmohsen; Nabil, Emad
Automated scoring systems have been revolutionized by natural language processing, enabling the evaluation of students’ diverse answers across various academic disciplines. However, this presents a challenge as students’ responses may vary significantly in terms of length, structure, and content. To tackle this challenge, this research introduces a novel automated model for short answer grading. The proposed model uses pretrained “transformer” models, specifically T5, in con- junction with a BI-LSTM architecture which is effective in processing sequential data by considering the past and future context. This research evaluated several preprocessing techniques and different hyperparameters to identify the most efficient architecture. Experiments were conducted using a standard benchmark dataset named the North Texas Dataset. This research achieved a state-of-the-art correlation value of 92.5 percent. The proposed model’s accuracy has significant implications for education as it has the potential to save educators considerable time and effort, while providing a reliable and fair evaluation for students, ultimately leading to improved learning outcomes.
Short Answer Grading Using String Similarity And Corpus-Based Similarity
(SCIENCE & INFORMATION SAI ORGANIZATION LTD, 2012-11) Fahmy, Aly A; Gomaa, Wael H
Most automatic scoring systems use pattern based that requires a lot of hard and tedious work. These systems work in a supervised manner where predefined patterns and scoring rules are generated. This paper presents a different unsupervised approach which deals with students' answers holistically using text to text similarity. Different String-based and Corpus-based similarity measures were tested separately and then combined to achieve a maximum correlation value of 0.504. The achieved correlation is the best value achieved for unsupervised approach Bag of Words (BOW) when compared to previous work.