DeepCorrect: Building an Efficient Framework for Auto Correction for Subjective Questions Using GRU_LSTM Deep Learning
Diaa Salama AbdElminaam, Nada Ibrahim H. Ahmed, Mariam Yasser, Rawan Ahmed, Philip George, Mahmoud Sahhar · 2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) · 2022
Our lives have become increasingly reliant on social media. It has evolved into one of the most vital information sources. In this work, we propose machine learning and deep learning algorithms. LSTM (one to three layers) and GRU are two deep learning approaches (one to three layers). Six machine learning techniques are used to compare the performance of the proposed methodologies. Decision tree (DT), logistic regression (LR), K nearest neighbor(KNN), random forest (RF), support vector machine (SVM), and NaiveBayes (NB) are the six machine learning approaches. Keras-tuner is used to optimise the parameters of deep learning techniques, whereas a grid search is used to optimise the parameters of machine learning techniques. Three Benchmark datasets were used to train and test models. For the baseline machine learning model and word embedding feature extraction method for deep neural network methods, two feature extraction methods (TF-ID with N-gram) were utilised to extract critical features from the three benchmark datasets. The proposed deep learning techniques always show the best performance because of their ability to learn the discriminatory features through the multiple hidden layers. LSTM(one layer) showed the best cross-validation accuracy (66.79%) on the dataset. In the case of the LSTM(two layers) showed the best cross-validation accuracy (66.07%). Finally, GRU (two layers) showed the best cross-validation accuracy (66.73%). The propsed framework has been categorized into some steps. Experimental results on bench challenging datasets demonstrate that our methods can achieve better performance than numerous state-of-the-art methods.