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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2018: Intelligent Data Engineering and Automated Learning – IDEAL 2018 pp 107–116Cite as

Exploring Online Novelty Detection Using First Story Detection Models

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11314)

Abstract

Online novelty detection is an important technology in understanding and exploiting streaming data. One application of online novelty detection is First Story Detection (FSD) which attempts to find the very first story about a new topic, e.g. the first news report discussing the “Beast from the East” hitting Ireland. Although hundreds of FSD models have been developed, the vast majority of these only aim at improving the performance of the detection for some specific dataset, and very few focus on the insight of novelty itself. We believe that online novelty detection, framed as an unsupervised learning problem, always requires a clear definition of novelty. Indeed, we argue the definition of novelty is the key issue in designing a good detection model. Within the context of FSD, we first categorise online novelty detection models into three main categories, based on different definitions of novelty scores, and then compare the performances of these model categories in different features spaces. Our experimental results show that the challenge of FSD varies across novelty scores (and corresponding model categories); and, furthermore, that the detection of novelty in the very popular Word2Vec feature space is more difficult than in a normal frequency-based feature space because of a loss of word specificity.

Keywords

  • Online novelty detection
  • First Story Detection (FSD)
  • Unsupervised learning
  • Novelty score
  • Feature space
  • Word2Vec

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Acknowledgement

The authors wish to acknowledge the support of the ADAPT Research Centre. The ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Funds.

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Authors and Affiliations

  1. School of Computing, Dublin Institute of Technology, Dublin, Ireland

    Fei Wang, Robert J. Ross & John D. Kelleher

  2. ADAPT Research Centre, Dublin, Ireland

    Fei Wang, Robert J. Ross & John D. Kelleher

Authors
  1. Fei Wang
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  2. Robert J. Ross
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  3. John D. Kelleher
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Corresponding author

Correspondence to Fei Wang .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Dr. Hujun Yin

  2. Autonomous University of Madrid, Madrid, Spain

    David Camacho

  3. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

  4. University of Seville, Seville, Spain

    Dr. Antonio J. Tallón-Ballesteros

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Wang, F., Ross, R.J., Kelleher, J.D. (2018). Exploring Online Novelty Detection Using First Story Detection Models. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-03493-1_12

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