Learning Inter-Lingual Document Representations via Concept Compression

Marc Lenz*, Tsegaye Misikir Tashu, Tomáš Horváth

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    1 Citation (Scopus)

    Abstract

    In this work, we proposed a novel approach to derive inter-lingual document representations. The introduced methods aim to enhance the quality of content-based Multilingual Document Recommendation and information retrieval Systems. The main idea centers around creating inter-lingual representations by using mappings to align monolingual representation spaces. According to the experimental results carried out on JRC-Acquis and EU bookshop multilingual corpora, the proposed concept compression approach has outperformed the traditional cross-lingual retrieval and recommendations methods.

    Original languageEnglish
    Title of host publicationIntelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings
    EditorsDavid Camacho, Peter Tino, Richard Allmendinger, Hujun Yin, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento
    Place of PublicationCham
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages268-276
    Number of pages9
    ISBN (Electronic)978-3-030-91608-4
    ISBN (Print)9783030916077
    DOIs
    Publication statusPublished - 23-Nov-2021
    Event22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021 - Virtual, Online
    Duration: 25-Nov-202127-Nov-2021

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume13113 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021
    CityVirtual, Online
    Period25/11/202127/11/2021

    Keywords

    • Cross-lingual document representation
    • Cross-lingual information retrieval
    • Multilingual NLP

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