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Vec2graph: A Python Library for Visualizing Word Embeddings as Graphs

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Analysis of Images, Social Networks and Texts (AIST 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1086))

Abstract

Visualization as a means of easy conveyance of ideas plays a key role in communicating linguistic theory through its applications. User-friendly NLP visualization tools allow researchers to get important insights for building, challenging, proving or rejecting their hypotheses. At the same time, visualizations provide general public with some understanding of what computational linguists investigate.

In this paper, we present vec2graph: a ready-to-use Python 3 library visualizing vector representations (for example, word embeddings) as dynamic and interactive graphs. It is aimed at users with beginners’ knowledge of software development, and can be used to easily produce visualizations suitable for the Web. We describe key ideas behind vec2graph, its hyperparameters, and its integration into existing word embedding frameworks.

N. Katricheva and A. Yaskevich—Contributed equally to the paper.

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Notes

  1. 1.

    All English word embedding models used were downloaded from the NLPL Vectors repository [4].

  2. 2.

    All Russian word embeddings used were downloaded from RusVectores service [9].

  3. 3.

    https://projector.tensorflow.org/.

  4. 4.

    https://github.com/anvaka/word2vec-graph.

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Author information

Authors and Affiliations

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

    Nadezda Katricheva, Alyaxey Yaskevich, Anastasiya Lisitsina & Tamara Zhordaniya

  2. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  3. University of Trento, Trento, Italy

    Elizaveta Kuzmenko

Authors
  1. Nadezda Katricheva
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  2. Alyaxey Yaskevich
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  3. Anastasiya Lisitsina
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  4. Tamara Zhordaniya
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  5. Andrey Kutuzov
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  6. Elizaveta Kuzmenko
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Corresponding author

Correspondence to Nadezda Katricheva .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

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

    Dmitry I. Ignatov

  4. Institute of Mathematics and Mechanics Yekaterinburg, Yekaterinburg, Russia

    Michael Khachay

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

    Valentina Kuskova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

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

    Sergei O. Kuznetsov

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

    Irina A. Lomazova

  9. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. Loria, Vandoeuvre lès Nancy, France

    Amedeo Napoli

  11. University of Florida, Gainesville, USA

    Panos M. Pardalos

  12. Ca' Foscari University of Venice, Venezia Mestre, Italy

    Marcello Pelillo

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Katricheva, N., Yaskevich, A., Lisitsina, A., Zhordaniya, T., Kutuzov, A., Kuzmenko, E. (2020). Vec2graph: A Python Library for Visualizing Word Embeddings as Graphs. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Communications in Computer and Information Science, vol 1086. Springer, Cham. https://doi.org/10.1007/978-3-030-39575-9_20

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  • DOI: https://doi.org/10.1007/978-3-030-39575-9_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39574-2

  • Online ISBN: 978-3-030-39575-9

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