TABAS: Text augmentation based on attention score for text classification model
| Autor: | Jong Woo Kim, So Young Jun, Seung Joo Yoon, Yeong Jae Yu |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Machine learning computer.software_genre Convolutional neural network Artificial Intelligence Hardware and Architecture Benchmark (computing) Artificial intelligence business computer Software Word (computer architecture) Information Systems |
| Zdroj: | ICT Express. 8:549-554 |
| ISSN: | 2405-9595 |
| DOI: | 10.1016/j.icte.2021.11.002 |
| Popis: | To improve the performance of text classification, we propose text augmentation based on attention score (TABAS). We recognized that a criterion for selecting a replacement word rather than a random selection was necessary. Therefore, TABAS utilizes attention scores for text modification, processing only words with the same entity and part-of-speech tags to consider informational aspects. To verify this approach, we used two benchmark tasks. As a result, TABAS can significantly improve performance, both recurrent and convolutional neural networks. Furthermore, we confirm that it provides a practical way to develop deep-learning models by saving costs on making additional datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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