Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150087
Title: Deep learning techniques for text classification
Authors: Raihan, Diardano
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Document and text processing
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Raihan, D. (2021). Deep learning techniques for text classification. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150087
Project: D-204-19201-02750
Abstract: This dissertation presents a series of experiments in applying deep learning techniques for text classification. The experiment will evaluate the performance of some popular deep learning models, such as feedforward, recurrent, convolutional, and ensemble-based neural networks, on five different datasets. We will build each model on top of two separate feature extractions to capture information within the text. The result shows that the word embedding provides a robust feature extractor to all the models in making a better final prediction. The experiment also highlights the effectiveness of the ensemble-based and temporal convolutional neural network in achieving good performances and even competing with the state-of-the-art benchmark models.
URI: https://hdl.handle.net/10356/150087
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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Evaluate the performance of several state-of-the-art deep learning models on various text classification datasets.2.67 MBAdobe PDFView/Open

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