Linear Concept Approximation for Multilingual Document Recommendation

Vilmos Tibor Salamon, 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 paper, we proposed Linear Concept Approximation, a novel multilingual document representation approach for the task of multilingual document representation and recommendation. The main idea is in creating representations by using mappings to align monolingual representation spaces using linear concept approximation, that in turn will enhance the quality of content-based Multilingual Document Recommendation Systems. The experimental results on JRC-Acquis have shown that our proposed approach outperformed traditional methods on the task of multilingual document recommendation.

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
PublisherSpringer Science and Business Media Deutschland GmbH
Pages147-156
Number of pages10
ISBN (Print)9783030916077
DOIs
Publication statusPublished - 23-Nov-2021
Externally publishedYes
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

  • Document recommendation
  • Latent semantic indexing
  • Multilingual NLP
  • Multilingual representation learning

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