A Trade-off between ML and DL Techniques in Natural Language Processing

Abstract The domain of Natural Language Processing covers various tasks, such as classification, text generation, and language model. data processed using word embeddings, or vectorizers, is then trained Machine Learning Deep algorithms. In order to observe the tradeoff between both these types algorithms, with respect available, accuracy obtained other factors, a binary classification undertaken distinguish insincere regular questions on Quora. A dataset called Quora Insincere Questions Classification was used train machine learning deep models. Bidirectional-Long Short Term Network (LSTM) trained, Global Vectors for Word Representation (GloVe). algorithms Extreme Gradient Boosting classifier, Gaussian Naive Bayes, Support Vector Classifier (SVC), by TF-IDF vectorizer process text. This paper also presents an evaluation above basis precision, recall, f1 score metrics.