Kider: Knowledge-infused document embedding representation for text categorization

Yu Ting Chen, Zheng Wen Lin, Yung Chun Chang, Wen Lian Hsu

研究成果: 書貢獻/報告類型會議貢獻

摘要

Advancement of deep learning has improved performances on a wide variety of tasks. However, language reasoning and understanding remain difficult tasks in Natural Language Processing (NLP). In this work, we consider this problem and propose a novel Knowledge-Infused Document Embedding Representation (KIDER) for text categorization. We use knowledge patterns to generate high quality document representation. These patterns preserve categorical-distinctive semantic information, provide interpretability, and achieve superior performances at the same time. Experiments show that the KIDER model outperforms state-of-the-art methods on two important NLP tasks, i.e., emotion analysis and news topic detection, by 7% and 20%. In addition, we also demonstrate the potential of highlighting important information for each category and news using these patterns. These results show the value of knowledge-infused patterns in terms of interpretability and performance enhancement.

原文英語
主出版物標題Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings
編輯Hamido Fujita, Jun Sasaki, Philippe Fournier-Viger, Moonis Ali
發行者Springer Science and Business Media Deutschland GmbH
頁面18-29
頁數12
ISBN(列印)9783030557881
DOIs
出版狀態已發佈 - 2020
事件33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020 - Kitakyushu, 日本
持續時間: 九月 22 2020九月 25 2020

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12144 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

會議

會議33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020
國家日本
城市Kitakyushu
期間9/22/209/25/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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