Description
Title: SYNONYMS AND MAXIMUM LOSS FOR TEXTUAL DATA AUGMENTATION FOR NEURAL NETWORKS
Abstract: One method for addressing labeled data scarcity and over-tting is data augmentation. Modern deep-learning algorithms, which require enormous amounts of data, depend on both of these issues. The issue can be better understood in the context of image analysis than in the context of text, and this work fills a gap in that understanding. We suggest a technique for enhancing textual data when convolutional neural networks are being trained for sentence classification. The augmentation relies on word substitution using a thesaurus and WordNet from Princeton University. Most of the time, our method outperforms the standard. The best variant outperforms the baseline by 1.2% (pp.) in terms of accuracy.
Keywords: deep learning, data augmentation, neural networks, natural language processing, sentence classi cation
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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