Research Output
NCC: Neural concept compression for multilingual document recommendation
  In this work, we propose a novel method for generating inter-lingual document representations using neural network concept compression. The presented approach is intended to improve the quality of content-based multilingual document recommendation and information retrieval systems by creating a language-independent representation. The main idea is to use mappings to align monolingual representation spaces, using concept compression, to create inter-lingual representations. The proposed approach outperforms traditional cross-lingual retrieval and recommendations methods in experiments conducted on JRC-Acquis and EU bookshop multilingual corpora. Our dataset and code are publicly available at https://github.com/Tsegaye-misikir/NCC.

  • Type:

    Conference Paper

  • Date:

    27 April 2023

  • Publication Status:

    Published

  • Publisher

    Elsevier BV

  • DOI:

    10.1016/j.asoc.2023.110348

  • ISSN:

    1568-4946

  • Funders:

    Historic Funder (pre-Worktribe)

Citation

Tashu, T. M., Lenz, M., & Horváth, T. (2023). NCC: Neural concept compression for multilingual document recommendation. Applied Soft Computing, 142, Article 110348. https://doi.org/10.1016/j.asoc.2023.110348

Authors

Keywords

Information retrieval, Document representation, Natural language processing, Cross-lingual representation, Multi-lingual recommendation

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