Skip to main content
Book cover

International Conference on Analysis of Images, Social Networks and Texts

AIST 2016: Analysis of Images, Social Networks and Texts pp 155–161Cite as

WebVectors: A Toolkit for Building Web Interfaces for Vector Semantic Models

Part of the Communications in Computer and Information Science book series (CCIS,volume 661)

Abstract

The paper presents a free and open source toolkit which aim is to quickly deploy web services handling distributed vector models of semantics. It fills in the gap between training such models (many tools are already available for this) and dissemination of the results to general public. Our toolkit, WebVectors, provides all the necessary routines for organizing online access to querying trained models via modern web interface. We also describe two demo installations of the toolkit, featuring several efficient models for English, Russian and Norwegian.

Keywords

  • Distributional semantics
  • Neural embeddings
  • Word2vec
  • Machine learning
  • Visualization

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-52920-2_15
  • Chapter length: 7 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-52920-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.

Notes

  1. 1.

    https://github.com/akutuzov/webvectors.

  2. 2.

    As of now, WebVectors supports models in generic Word2vec format (which is essentially a simple list of word vectors, in text or binary form) and gensim format (it is always binary and retains much more technical data, including output vectors).

References

  1. Turney, P.D., Pantel, P., et al.: From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37(1), 141–188 (2010)

    MathSciNet  MATH  Google Scholar 

  2. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 3111–3119 (2013)

    Google Scholar 

  3. Siencnik, S.K.: Adapting word2vec to named entity recognition. In: Nordic Conference of Computational Linguistics, NODALIDA 2015, p. 239 (2015)

    Google Scholar 

  4. Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 142–150. Association for Computational Linguistics (2011)

    Google Scholar 

  5. Zou, W.Y., Socher, R., Cer, D.M., Manning, C.D.: Bilingual word embeddings for phrase-based machine translation. In: EMNLP, pp. 1393–1398 (2013)

    Google Scholar 

  6. Kutuzov, A., Kuzmenko, E.: Comparing neural lexical models of a classic national corpus and a web corpus: the case for Russian. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9041, pp. 47–58. Springer, Heidelberg (2015). doi:10.1007/978-3-319-18111-0_4

    Google Scholar 

  7. Baroni, M., Dinu, G., Kruszewski, G.: Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1 (2014)

    Google Scholar 

  8. Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Valletta, Malta, ELRA, pp. 45–50, May 2010

    Google Scholar 

  9. Padró, L., Stanilovsky, E.: Freeling 3.0: towards wider multilinguality. In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012), Istanbul, Turkey, European Language Resources Association (ELRA), May 2012

    Google Scholar 

  10. Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(2579–2605), 85 (2008)

    MATH  Google Scholar 

  11. Kutuzov, A., Andreev, I.: Texts in, meaning out: neural language models in semantic similarity task for Russian. In: Proceedings of the Dialog Conference, Moscow, RGGU (2015)

    Google Scholar 

  12. Mikolov, T., Le, Q., Sutskever, I.: Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168 (2013)

  13. Hofland, K.: A self-expanding corpus based on newspapers on the web. In: LREC (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  2. National Research University Higher School of Economics, Moscow, Russia

    Elizaveta Kuzmenko

Authors
  1. Andrey Kutuzov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elizaveta Kuzmenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elizaveta Kuzmenko .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  2. Krasovsky Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Mikhail Yu. Khachay

  3. Ural Federal University, Yekaterinbug, Russia

    Valeri G. Labunets

  4. Research Computing Center, Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Sergey I. Nikolenko

  6. Technische Universität Darmstadt, Darmstadt, Germany

    Alexander Panchenko

  7. Laboratory of Algorithms and Technologies for Networks Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  8. Dorodnicyn Computing Centre of Russian Academy of Sciences, Moscow, Russia

    Konstantin Vorontsov

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Kutuzov, A., Kuzmenko, E. (2017). WebVectors: A Toolkit for Building Web Interfaces for Vector Semantic Models. In: , et al. Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-52920-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-52920-2_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52919-6

  • Online ISBN: 978-3-319-52920-2

  • eBook Packages: Computer ScienceComputer Science (R0)