The highest average micro F 1-score achieved 0.8 when applied GloVe by using CNN model on adding focusing tags in the Thai sentences dataset. The classifying question categories achieved with average micro F 1-score of 0.98 when applied SVM model on adding all POS tags in the TREC-6 dataset. The experiment results showed that our methodology based on Part-of-Speech (POS) tagging was the best to improve question classification accuracy. The deep learning techniques were Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNN), and Hybrid model, which combined CNN and BiLSTM model.
The traditional classification models were Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine. Machine learning models based on traditional and deep learning classifiers were used. We conducted several experiments to evaluate the performance of the proposed methodology using two different datasets (TREC-6 dataset and Thai sentence dataset) with term frequency and combined term frequency-inverse document frequency including Unigram, Unigram+Bigram, and Unigram + Trigram as features. In this paper, we proposed a methodology to improve question classification from texts by using feature selection and word embedding techniques. Question classification could help define the structure of question sentences generated by features extraction from a sentence, such as who, when, where, and how. Question classification is a crucial task for answer selection.
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