Improving Text Classification Using Knowledge in Labels

Cheng Zhang, Hayato Yamana

研究成果: Conference contribution

抄録

Various algorithms and models have been proposed to address text classification tasks; however, they rarely consider incorporating the additional knowledge hidden in class labels. We argue that hidden information in class labels leads to better classification accuracy. In this study, instead of encoding the labels into numerical values, we incorporated the knowledge in the labels into the original model without changing the model architecture. We combined the output of an original classification model with the relatedness calculated based on the embeddings of a sequence and a keyword set. A keyword set is a word set to represent knowledge in the labels. Usually, it is generated from the classes while it could also be customized by the users. The experimental results show that our proposed method achieved statistically significant improvements in text classification tasks. The source code and experimental details of this study can be found on Github11https://github.com/HeroadZ/KiL.

本文言語English
ホスト出版物のタイトル2021 IEEE 6th International Conference on Big Data Analytics, ICBDA 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ193-197
ページ数5
ISBN(電子版)9780738131672
DOI
出版ステータスPublished - 2021 3 5
イベント6th IEEE International Conference on Big Data Analytics, ICBDA 2021 - Xiamen, China
継続期間: 2021 3 52021 3 8

出版物シリーズ

名前2021 IEEE 6th International Conference on Big Data Analytics, ICBDA 2021

Conference

Conference6th IEEE International Conference on Big Data Analytics, ICBDA 2021
国/地域China
CityXiamen
Period21/3/521/3/8

ASJC Scopus subject areas

  • 情報システム
  • 情報システムおよび情報管理
  • 人工知能
  • コンピュータ サイエンスの応用

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