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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Lexical Data Augmentation for Text Classification in Deep Learning
verfasst von : Rong Xiang, Emmanuele Chersoni, Yunfei Long, Qin Lu, Chu-Ren Huang
Erschienen in: Advances in Artificial Intelligence
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Abstract
This paper presents our work on using part-of-speech focused lexical substitution for data augmentation (PLSDA) to enhance the prediction capabilities and the performance of deep learning models. This paper explains how PLSDA uses part-of-speech information to identify words and make use of different augmentation strategies to find semantically related substitutions to generate new instances for training. Evaluations of PLSDA is conducted on a variety of datasets across different text classification tasks. When PLSDA is applied to four deep learning models, results show that classifiers trained with PLSDA achieve 1.3% accuracy improvement on average.