Artificial Intelligence and Decision Making
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Artificial Intelligence and Decision Making, 2022, Issue 2, Pages 27–35
DOI: https://doi.org/10.14357/20718594220203 (Mi iipr62) 		 
Analysis of textual and graphical information

Methods for cross-lingual retrieval of similar documents in legal domain based on machine learning

V. V. Zhebela, D. A. Devyatkinb, D. V. Zubarevb, I. V. Sochenkovbcd

a Limited liability company "Technologies for Systems Analysis", Moscow, Russia
b Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
c Innopolis University, Kazan, Russia
d Ivannikov Institute for System Programming of the RAS, Moscow, Russia
Full-text PDF (461 kB)
DOI: https://doi.org/10.14357/20718594220203
Abstract: The need of studying the international experience to improve legislation cause the need of information retrieval systems to be good in multilingual legal domain. One of the possible solutions is thematically similar document retrieval. However, there is an important task to transfer between languages to let the user put a document on the one language and get the search result on another one. The paper describes different approaches to solve this problem: from classical mediator-based methods to modern procedures of distributive semantics. As a test collection, we have used the UN digital library. The combination of the extended translation model and BM25 ranking function demonstrates the best results.
Keywords: cross-lingual document retrieval, distributional semantics, information retrieval in the legal domain.
Funding agency Grant number
Russian Foundation for Basic Research 18-29-16172
English version:
Scientific and Technical Information Processing, 2023, Volume 50, Issue 5, Pages 494–499
DOI: https://doi.org/10.3103/S0147688223050167
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: V. V. Zhebel, D. A. Devyatkin, D. V. Zubarev, I. V. Sochenkov, “Methods for cross-lingual retrieval of similar documents in legal domain based on machine learning”, Artificial Intelligence and Decision Making, 2022, no. 2, 27–35; Scientific and Technical Information Processing, 50:5 (2023), 494–499
Citation in format AMSBIB
\Bibitem{ZheDevZub22}
\by V.~V.~Zhebel, D.~A.~Devyatkin, D.~V.~Zubarev, I.~V.~Sochenkov
\paper Methods for cross-lingual retrieval of similar documents in legal domain based on machine learning
\jour Artificial Intelligence and Decision Making
\yr 2022
\issue 2
\pages 27--35
\mathnet{http://mi.mathnet.ru/iipr62}
\crossref{https://doi.org/10.14357/20718594220203}
\elib{https://elibrary.ru/item.asp?id=48707183}
\transl
\jour Scientific and Technical Information Processing
\yr 2023
\vol 50
\issue 5
\pages 494--499
\crossref{https://doi.org/10.3103/S0147688223050167}
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