A semantic frame-based intelligent agent for topic detection

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...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2017-01, Vol.21 (2), p.391-401
Main Authors: Chang, Yung-Chun, Hsieh, Yu-Lun, Chen, Cen-Chieh, Hsu, Wen-Lian
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computational Intelligence
Control
Documents
Engineering
Focus
Intelligent agents
Knowledge management
Labeling
Machine learning
Mathematical Logic and Foundations
Mechatronics
Methods
News
Ontology
Performance evaluation
Robotics
Semantic web
Semantics
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Summary: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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-015-1695-4