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Unsupervised Neural Aspect Extraction with Related Terms

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Artificial Intelligence and Natural Language (AINL 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1292))

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Abstract

The tasks of aspect identification and term extraction remain challenging in natural language processing. While supervised methods achieve high scores, it is hard to use them in real-world applications due to the lack of labelled datasets. Unsupervised approaches outperform these methods on several tasks, but it is still a challenge to extract both an aspect and a corresponding term, particularly in the multi-aspect setting. In this work, we present a novel unsupervised neural network with convolutional multi-attention mechanism, that allows extracting pairs (aspect, term) simultaneously, and demonstrate the effectiveness on the real-world dataset. We apply a special loss aimed to improve the quality of multi-aspect extraction. The experimental study demonstrates, what with this loss we increase the precision not only on this joint setting but also on aspect prediction only.

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Acknowledgements

This research is financially supported by The Russian Science Foundation, Agreement #20-11-20270.

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Authors and Affiliations

  1. ITMO University, St. Petersburg, Russia

    Timur Sokhin, Maria Khodorchenko & Nikolay Butakov

Authors
  1. Timur Sokhin
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  2. Maria Khodorchenko
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  3. Nikolay Butakov
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Corresponding author

Correspondence to Timur Sokhin .

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Editors and Affiliations

  1. ITMO University, St. Petersburg, Russia

    Andrey Filchenkov

  2. Haaga-Helia University of Applied Sciences, Helsinki, Finland

    Janne Kauttonen

  3. University of Helsinki, Helsinki, Finland

    Lidia Pivovarova

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Sokhin, T., Khodorchenko, M., Butakov, N. (2020). Unsupervised Neural Aspect Extraction with Related Terms. In: Filchenkov, A., Kauttonen, J., Pivovarova, L. (eds) Artificial Intelligence and Natural Language. AINL 2020. Communications in Computer and Information Science, vol 1292. Springer, Cham. https://doi.org/10.1007/978-3-030-59082-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-59082-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59081-9

  • Online ISBN: 978-3-030-59082-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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