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A semantic frame-based intelligent agent for topic detection

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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.

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Notes

  1. http://www-nlp.stanford.edu/ner/.

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    Architecture of named entity ontology

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Acknowledgments

This research was supported by the Ministry of Science and Technology of Taiwan under grant MOST 103-3111-Y-001-027.

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Authors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

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

  2. Department of Information Management, National Taiwan University, Taipei, Taiwan

    Yung-Chun Chang

  3. Department of Computer Science, National Chengchi University, Taipei, Taiwan

    Yu-Lun Hsieh & Cen-Chieh Chen

Authors
  1. Yung-Chun Chang
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  2. Yu-Lun Hsieh
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  3. Cen-Chieh Chen
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  4. Wen-Lian Hsu
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Corresponding author

Correspondence to Yung-Chun Chang.

Additional information

Communicated by C.-S. Lee.

This research was supported by the National Science Council of Taiwan under Grant NSC102-3111-Y-001-012, NSC102-3113-P-001-006 and NSC 102-3114-Y-307-026.

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Chang, YC., Hsieh, YL., Chen, CC. et al. A semantic frame-based intelligent agent for topic detection. Soft Comput 21, 391–401 (2017). https://doi.org/10.1007/s00500-015-1695-4

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