A semantic frame-based intelligent agent for topic detection

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

Research output: Contribution to journalArticle

6 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 propose a semantic frame-based topic detection (SFTD) that simulates such process in human perception. We take advantage of multiple knowledge sources and extracted discriminative patterns from documents through a highly automated, knowledge-supported frame generation and matching mechanisms. Using a Chinese news corpus containing over 111,000 news articles, we provide a comprehensive performance evaluation which demonstrates 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. Experimental results show that SFTD is comparable to other well-known topic detection methods.

Original languageEnglish
Pages (from-to)391-401
Number of pages11
JournalSoft Computing
Volume21
Issue number2
DOIs
Publication statusPublished - Jan 1 2017
Externally publishedYes

Keywords

  • Partial matching
  • Semantic class
  • Semantic frame
  • Topic detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology

Fingerprint Dive into the research topics of 'A semantic frame-based intelligent agent for topic detection'. Together they form a unique fingerprint.

  • Cite this