Semantic frame-based natural language understanding for intelligent topic detection agent

Yung Chun Chang, Yu Lun Hsieh, Cen Chieh Chen, Wen Lian Hsu

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

3 Citations (Scopus)

Abstract

Detecting the topic of documents can help readers construct the background of the topic and facilitate document comprehension. In this paper, we proposed a semantic frame-based method for topic detection that simulates such process in human perception. We took advantage of multiple knowledge sources and identified discriminative patterns from documents through frame generation and matching mechanisms. Results demonstrated that our novel approach can effectively detect the topic of a document by exploiting the syntactic structures, semantic association, and the context within the text. Moreover, it also outperforms well-known topic detection methods.

Original languageEnglish
Title of host publicationModern Advances in Applied Intelligence - 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Proceedings
PublisherSpringer Verlag
Pages339-348
Number of pages10
Volume8481
EditionPART 1
ISBN (Electronic)9783319074542
ISBN (Print)9783319074542
DOIs
Publication statusPublished - Jan 1 2014
Externally publishedYes
Event27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014 - Kaohsiung, Taiwan
Duration: Jun 3 2014Jun 6 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8481 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014
CountryTaiwan
CityKaohsiung
Period6/3/146/6/14

Keywords

  • Partial Matching
  • Semantic Class
  • Semantic Frame
  • Sequence Alignment
  • Topic Detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Chang, Y. C., Hsieh, Y. L., Chen, C. C., & Hsu, W. L. (2014). Semantic frame-based natural language understanding for intelligent topic detection agent. In Modern Advances in Applied Intelligence - 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Proceedings (PART 1 ed., Vol. 8481, pp. 339-348). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8481 LNAI, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-319-07455-9_36