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Serelex: Search and Visualization of Semantically Related Words

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Book cover Advances in Information Retrieval (ECIR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7814))

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Abstract

We present Serelex, a system that provides, given a query in English, a list of semantically related words. The terms are ranked according to an original semantic similarity measure learnt from a huge corpus. The system performs comparably to dictionary-based baselines, but does not require any semantic resource such as WordNet. Our study shows that users are completely satisfied with 70% of the query results.

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Author information

Authors and Affiliations

  1. Université catholique de Louvain, Louvain-la-Neuve, Belgium

    Alexander Panchenko, Olga Morozova, Hubert Naets & Cédrick Fairon

  2. Bauman Moscow State Technical University, Moscow, Russia

    Alexander Panchenko, Pavel Romanov, Andrey Philippovich & Alexey Romanov

Authors
  1. Alexander Panchenko
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  2. Pavel Romanov
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  3. Olga Morozova
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  4. Hubert Naets
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  5. Andrey Philippovich
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  6. Alexey Romanov
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  7. Cédrick Fairon
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Editor information

Editors and Affiliations

  1. Yandex, Leo Tolstoy, 16, 119021, Moscow, Russia

    Pavel Serdyukov  & Ilya Segalovich  & 

  2. Kontur Labs and Ural Federal University, Fonvizina 3-27, 620078, Yekaterinburg, Russia

    Pavel Braslavski

  3. National Research University Higher School of Economics (HSE), Pokrovskii bd 11, 109028, Moscow, Russia

    Sergei O. Kuznetsov

  4. University of Amsterdam, Turfdraagsterpad 9, 1012 XT, Amsterdam, The Netherlands

    Jaap Kamps

  5. Knowledge Media Institute, The Open University, Walton Hall, MK7 6AA, Milton Keynes, UK

    Stefan Rüger

  6. Mathematics & Computer Science Department, Emory University, 400 dowman Drive, 30329, Atlanta, GA, USA

    Eugene Agichtein

  7. Department of Computer Science, University College London, Gower Street, WC1E 6BT, London, UK

    Emine Yilmaz

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© 2013 Springer-Verlag Berlin Heidelberg

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Panchenko, A. et al. (2013). Serelex: Search and Visualization of Semantically Related Words. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_97

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  • DOI: https://doi.org/10.1007/978-3-642-36973-5_97

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36972-8

  • Online ISBN: 978-3-642-36973-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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