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Factuality Classification Using Multi-facets Based on Elementary Discourse Units for News Articles

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Recent Advances and Future Prospects in Knowledge, Information and Creativity Support Systems (KICSS 2015)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 685))

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Abstract

Factuality classification is used for classifying information based on degrees of certainty. It has been actively used in different applications including information extraction, textual entailment, finding semantic uncertainty and certainty, or fact extraction. In this paper, we propose an approach to improve factuality classification by analyzing information in Elementary Discourse Units (EDUs) and their relations. We use news articles as our case study since it contains information that has various degrees of certainty or factuality values (i.e., information about certain events or uncertain information from factual and opinionated information). In this work, we use five sets of facets for factuality classification, which are (1) Epistemic Modality set, (2) Subjectivity Type set, (3) Rhetorical Structure Theory (RST) set, (4) Semantic Implicative and Factive Patterns set and (5) Weasel Words set. Unlike previous works on factuality classification, we use multiple facets of EDU to examine certainty and unambiguity level of information. We performed experiments based on news articles in FactBank corpus. We evaluated our method by comparing with several state-of-the-art factuality classification techniques and the results clearly show that our method can improve accuracy in terms of precision, recall and F1-measure as 94.1%, 93.9% and 93.9%, respectively.

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Acknowledgement

This research was supported by the Center of Excellence in Intelligent Informatics, Speech and Language Technology and Service Innovation (CILS), Intelligent Informatics and Service Innovation (IISI) and NRU grant at SIIT, Thammasat University.

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Authors and Affiliations

  1. School of Information, Computer and Communication Technology (ICT), Thammasat University, Khlong Nung, Thailand

    Khaing Swe Wynn

  2. Sirindhorn International Institute of Technology (SIIT), Thammasat University, Khlong Nung, Thailand

    Sasiporn Usanavasin

Authors
  1. Khaing Swe Wynn
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  2. Sasiporn Usanavasin
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Corresponding author

Correspondence to Khaing Swe Wynn .

Editor information

Editors and Affiliations

  1. School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand

    Thanaruk Theeramunkong

  2. Department of Automatic Control and Biomedical Engineering, AGH University of Science and Technology, Kraków, Poland

    Andrzej M.J. Skulimowski

  3. Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi-shi, Ishikawa, Japan

    Takaya Yuizono

  4. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi-shi, Ishikawa, Japan

    Susumu Kunifuji

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Swe Wynn, K., Usanavasin, S. (2018). Factuality Classification Using Multi-facets Based on Elementary Discourse Units for News Articles. In: Theeramunkong, T., Skulimowski, A., Yuizono, T., Kunifuji, S. (eds) Recent Advances and Future Prospects in Knowledge, Information and Creativity Support Systems. KICSS 2015. Advances in Intelligent Systems and Computing, vol 685. Springer, Cham. https://doi.org/10.1007/978-3-319-70019-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-70019-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70018-2

  • Online ISBN: 978-3-319-70019-9

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