Text Augmentation Using Hierarchy-based Word Replacement (단어 계층 기반 텍스트 데이터 증강 방법론)

kimmuseong (김무성); Namgyu Kim (김남규)

doi:10.9708/jksci.2021.26.01.057

Text Augmentation Using Hierarchy-based Word Replacement

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(1), pp.57-67
DOI : 10.9708/jksci.2021.26.01.057
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : November 30, 2020
Accepted : January 12, 2021
Published : January 29, 2021

kimmuseong ¹, Namgyu Kim ¹

¹국민대학교

Accredited

ABSTRACT

Recently, multi-modal deep learning techniques that combine heterogeneous data for deep learning analysis have been utilized a lot. In particular, studies on the synthesis of Text to Image that automatically generate images from text are being actively conducted. Deep learning for image synthesis requires a vast amount of data consisting of pairs of images and text describing the image. Therefore, various data augmentation techniques have been devised to generate a large amount of data from small data. A number of text augmentation techniques based on synonym replacement have been proposed so far. However, these techniques have a common limitation in that there is a possibility of generating a incorrect text from the content of an image when replacing the synonym for a noun word. In this study, we propose a text augmentation method to replace words using word hierarchy information for noun words. Additionally, we performed experiments using MSCOCO data in order to evaluate the performance of the proposed methodology.

KEYWORDS

Deep Learning, Generative Adversarial Network, Text to Image Synthesis, Data Augmentation, WordNet

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Text Augmentation Using Hierarchy-based Word Replacement

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* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Text Augmentation Using Hierarchy-based Word Replacement

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.