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This paper evaluates traditional and embedding-based methods for classifying question-answer pairs, comparing Bag-of-Words, TF-IDF, and various word embeddings using classical and deep learning models. The results indicate that TF-IDF outperforms Bag-of-Words, with deep learning models like BiLSTM and GRU yielding the highest accuracy and F1 scores, particularly when using GloVe embeddings. Overall, the study highlights the effectiveness of embedding-driven recurrent models for question-answer classificatio

Author

rifah.jahan.tamanna

Language

EN