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Spatial Information Recognition in Web Documents Using a Semi-supervised Machine Learning Method

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Web Information Systems Engineering – WISE 2017 (WISE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10569))

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Abstract

Web documents are a promising source of spatial information. With information recognition and extraction, this information can be used in various applications such as building semantic maps and indoor robotic navigation. In this paper, we present a novel methodology to identify spatial information in web documents using semi-supervised trained machine learning classifiers. The semi-supervised models trained with the half amount of data available yield only the F-score of 4% and 9% inferior to the supervised models trained with complete data on classifying spatial entities and relationships respectively.

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Notes

  1. 1.

    http://lostoncampus.com.au.

  2. 2.

    http://www.columbia.edu/content/self-guided-walking-tour.html.

  3. 3.

    http://www.unimelb.edu.au/campustour/self-guided.

  4. 4.

    http://www.bristol.ac.uk/university/visit/walking-tour.html.

  5. 5.

    http://www.nltk.org/api/nltk.tokenize.html#module-nltk.tokenize.punkt.

  6. 6.

    https://www.cs.cornell.edu/people/tj/svm_light/svm_hmm.html.

  7. 7.

    http://nlp.stanford.edu/projects/project-ner.shtml.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia

    Hendi Lie, Richi Nayak & Gordon Wyeth

Authors
  1. Hendi Lie
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  2. Richi Nayak
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  3. Gordon Wyeth
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Corresponding author

Correspondence to Hendi Lie .

Editor information

Editors and Affiliations

  1. University of Sydney, Darlington, NSW, Australia

    Athman Bouguettaya

  2. Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Institute of Computing for Physics and Technology, Protvino, Russia

    Andrey Klimenko

  4. Nanyang Technological University, Singapore, Singapore

    Lu Chen

  5. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Xiangliang Zhang

  6. Institute of Computing for Physics and Technology, Protvino, Russia

    Fedor Dzerzhinskiy

  7. Shanghai Jiao Tong University, Minhang Qu, China

    Weijia Jia

  8. Institute of Computing for Physics and Technology, Protvino, Russia

    Stanislav V. Klimenko

  9. City University of Hong Kong, Kowloon, Hong Kong

    Qing Li

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Lie, H., Nayak, R., Wyeth, G. (2017). Spatial Information Recognition in Web Documents Using a Semi-supervised Machine Learning Method. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10569. Springer, Cham. https://doi.org/10.1007/978-3-319-68783-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-68783-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68782-7

  • Online ISBN: 978-3-319-68783-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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