DEEP LEARNING-BASED TEXT CLASSIFICATION ALGORITHMS
Abstract
Text classification is a critical task in natural language processing (NLP) that involves categorizing text into predefined labels. With the advent of deep learning, text classification algorithms have seen significant improvements in accuracy and efficiency. This thesis explores various deep learning-based text classification algorithms, detailing the processes involved in dataset preparation, model architecture, training, and evaluation. Emphasis is placed on practical applications and the comparative performance of different models.
References
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. EMNLP.
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is All You Need. NIPS.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.
