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Explainable Document Classification via Pattern Structures

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Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23) (IITI 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 776))

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Abstract

Inherently explainable Machine Learning (ML) models are able to provide explanations for their predictions by virtue of their construction. The explanations of a ML model are more comprehensible if they are expressed in terms of its input features. Our paper proposes an inherently explainable pipeline for document classification using pattern structures and Abstract Meaning Representation (AMR) graphs. The pipeline generates two kinds of explanations: intermediate and final ones, that justify its classifications. Intermediate explanations are represented as significant subgraphs found in the document graphs of test documents. Final explanations are the sentences of the test documents, that correspond to the significant subgraphs.

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Notes

  1. 1.

    https://github.com/bjascob/amrlib.

  2. 2.

    https://github.com/bjascob/amrlib-models.

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Acknowledgements

The work of Sergei O. Kuznetsov on this paper was supported by the Russian Science Foundation under grant 22-11-00323 and performed at HSE University, Moscow, Russia.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Pokrovsky Blvd, 11, Moscow, 109028, Russia

    Sergei O. Kuznetsov & Eric George Parakal

Authors
  1. Sergei O. Kuznetsov
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  2. Eric George Parakal
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Correspondence to Sergei O. Kuznetsov .

Editor information

Editors and Affiliations

  1. Rostov State Transport University, Rostov-on-Don, Russia

    Sergey Kovalev

  2. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Igor Kotenko

  3. Rostov State Transport University, Rostov-on-Don, Russia

    Andrey Sukhanov

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Kuznetsov, S.O., Parakal, E.G. (2023). Explainable Document Classification via Pattern Structures. In: Kovalev, S., Kotenko, I., Sukhanov, A. (eds) Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). IITI 2023. Lecture Notes in Networks and Systems, vol 776. Springer, Cham. https://doi.org/10.1007/978-3-031-43789-2_39

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