Enhancing Text Classification Using Advanced Natural Language Processing Techniques
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Enhancing Text Classification Using Advanced Natural Language Processing Techniques
Archana H, Brijesh Jaya Poojary, Chayanka
Data Science and Computer Applications Manipal Institute of Technology
Manipal, India
Abstract—This study evaluates the performance of var- ious neural network models and a pre-trained trans- former model in the task of quote classification. The models analyzed include Fully Connected Neural Networks (FCNN), Long Short-Term Memory (LSTM), Gated Re- current Units (GRU), Bi-directional LSTM (BiLSTM), and DistilBERT. The goal is to identify the most effective model for the given dataset based on key performance metrics such as accuracy, precision, and recall. DistilBERT, a lightweight transformer-based model, is also assessed for its efficiency and accuracy compared to traditional neural network approaches.
Index Terms—Natural Language Processing (NLP), Data Preprocessing, Machine Learning, Quote Classifi- cation, Multi-label Classification, Neural Networks (NN), Transformer Models, DistilBERT.
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