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Utilizing Word Embeddings for Result Diversification in Tweet Search

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Information Retrieval Technology (AIRS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9460))

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Abstract

The performance of result diversification for tweet search suffers from the well-known vocabulary mismatch problem, as tweets are too short and usually informal. As a remedy, we propose to adopt a query and tweet expansion strategy that utilizes automatically-generated word embeddings. Our experiments using state-of-the-art diversification methods on the Tweets2013 corpus reveal encouraging results for expanding queries and/or tweets based on the word embeddings to improve the diversification performance in tweet search. We further show that the expansions based on the word embeddings may serve as useful as those based on a manually constructed knowledge base, namely, ConceptNet.

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Notes

  1. 1.

    http://conceptnet5.media.mit.edu/data/5.3/.

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Acknowledgement

This work is partially funded by METU under the grant number BAP-08-11-2013-055, and The Scientific and Technological Research Council of Turkey (TÃœBÄ°TAK) under the grant numbers 113E065 and 112E275.

Author information

Authors and Affiliations

  1. Middle East Technical University, Ankara, Turkey

    Kezban Dilek Onal, Ismail Sengor Altingovde & Pinar Karagoz

Authors
  1. Kezban Dilek Onal
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  2. Ismail Sengor Altingovde
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  3. Pinar Karagoz
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Corresponding author

Correspondence to Ismail Sengor Altingovde .

Editor information

Editors and Affiliations

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

    Guido Zuccon

  2. Brisbane, Queensland, Australia

    Shlomo Geva

  3. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  4. RMIT University, Melbourne, Australia

    Falk Scholer

  5. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Aixin Sun

  6. Tianjin University, China

    Peng Zhang

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Onal, K.D., Altingovde, I.S., Karagoz, P. (2015). Utilizing Word Embeddings for Result Diversification in Tweet Search. In: Zuccon, G., Geva, S., Joho, H., Scholer, F., Sun, A., Zhang, P. (eds) Information Retrieval Technology. AIRS 2015. Lecture Notes in Computer Science(), vol 9460. Springer, Cham. https://doi.org/10.1007/978-3-319-28940-3_29

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  • DOI: https://doi.org/10.1007/978-3-319-28940-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28939-7

  • Online ISBN: 978-3-319-28940-3

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