Kider: Knowledge-infused document embedding representation for text categorization

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationTrends 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
EditorsHamido Fujita, Jun Sasaki, Philippe Fournier-Viger, Moonis Ali
PublisherSpringer Science and Business Media Deutschland GmbH
Pages18-29
Number of pages12
ISBN (Print)9783030557881
DOIs
Publication statusPublished - 2020
Event33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020 - Kitakyushu, Japan
Duration: Sep 22 2020Sep 25 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12144 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020
CountryJapan
CityKitakyushu
Period9/22/209/25/20

Keywords

  • Knowledge representation
  • Natural Language Processing
  • Text categorization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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