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Multilingual Embeddings for Clustering Cultural Events

  • Conference paper
  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13217))

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Abstract

In the present paper we describe our approach to semi-automatic text annotation based on clustering. Given a large collection of announcements of cultural events from several websites, we group them based on their content and infer respective semantic categories that can be used for annotation (e.g. lecture, sports, food, music). We experiment with various models for vectorising the texts, including pretrained multilingual Sentence Transformers and multilingual ELMo models. The produced text embeddings are then clustered using K-means. We evaluate our clustering results using a stratified sample of texts with pre-existing categories (collected from websites listing the events) as well as intrinsic evaluation measures. The rationale behind this work is to produce a single categorisation covering texts from various sources and in two languages - English and Russian. The labelled collection of texts is intended for use in a Digital Humanities project aimed at describing cultural life in a selected location, for example, comparing types of events in Russian and British cities.

This work has been partly supported by the Russian Foundation for Basic Research within Project Cultural Trends in the Tyumen Region in the National and Global Contexts No. 20-411-720010 p_a_Tyumen region.

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Notes

  1. 1.

    Based on the previous results, the texts from Behance were excluded: They did not contain the descriptions of cultural events, but were mostly captions for graphics.

  2. 2.

    https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual, this model was chosen because it was fine-tuned on the semantic textual similarity task, and we want similar texts in two languages be placed in the same cluster.

  3. 3.

    https://github.com/ltgoslo/simple_elmo (model #219 in the NLPL repository http://vectors.nlpl.eu/repository/).

  4. 4.

    https://stats.stackexchange.com/questions/260487/adjusted-rand-index-vs-adjusted-mutual-information.

  5. 5.

    Algorithm settings: stratified 5-fold cv, earlystop=True; balanced=True.

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Author information

Authors and Affiliations

  1. University of Tyumen, Tyumen, Russia

    Maria Kunilovskaya

  2. University of Wolverhampton, Wolverhampton, UK

    Maria Kunilovskaya

  3. University of Trento, Trento, Italy

    Elizaveta Kuzmenko

Authors
  1. Maria Kunilovskaya
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  2. Elizaveta Kuzmenko
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Corresponding author

Correspondence to Maria Kunilovskaya .

Editor information

Editors and Affiliations

  1. Skolkovo Institute of Science and Technology, Moscow, Russia

    Evgeny Burnaev

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

    Dmitry I. Ignatov

  3. Skolkovo Institute of Science and Technology, Moscow, Russia

    Sergei Ivanov

  4. Krasovskii Institute of Mathematics and Mechanics of Russian Academy of Sciences, Yekaterinburg, Russia

    Michael Khachay

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

    Olessia Koltsova

  6. University of Oslo, Oslo, Norway

    Andrei Kutuzov

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

    Sergei O. Kuznetsov

  8. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  9. LORIA, Campus Scientifique, Vandœuvre lès Nancy, France

    Amedeo Napoli

  10. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  11. University of Florida, Gainesville, USA

    Panos M. Pardalos

  12. Aalto University, Espoo, Finland

    Jari Saramäki

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

    Andrey V. Savchenko

  14. Yandex LLC, Moscow, Russia

    Evgenii Tsymbalov

  15. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Kunilovskaya, M., Kuzmenko, E. (2022). Multilingual Embeddings for Clustering Cultural Events. In: Burnaev, E., et al. Analysis of Images, Social Networks and Texts. AIST 2021. Lecture Notes in Computer Science, vol 13217. Springer, Cham. https://doi.org/10.1007/978-3-031-16500-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-16500-9_8

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