nach oben

Scientific and Technical Information Processing

Erschienen in:

01.12.2023

Approaches to Cross-Language Retrieval of Similar Legal Documents Based on Machine Learning

verfasst von: V. V. Zhebel, D. A. Devyatkin, D. V. Zubarev, I. V. Sochenkov

Erschienen in: Scientific and Technical Information Processing | Ausgabe 5/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract—

In order to study global experience for legislation changing and rule-making necessitates, tools for information retrieval of regulatory documents written in different languages become increasingly necessary. One of the aspects of information identification is retrieval of thematically similar documents for a given input document. In this context, an important task of cross-lingual search arises when the user of an information system specifies a reference document in one language, and the search results contain relevant documents in other languages. The article describes different approaches to solving this problem: from classic mediator-based methods to more modern solutions, based on distributional semantics. The test collection used in the study was taken from the United Nations Digital Library, which provides legal documents in both the original English and their Russian translations.

Vorheriger Artikel Formalization of the Structural Synthesis of Technical Systems at the Initial Stage of Design

Nächster Artikel Reflex or Reflection? The Oculomotor Behavior of the Companion Robot, Creating the Impression of Communicating with an Emotional Being

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

https://digitallibrary.un.org/

https://www.wikipedia.org

http://www.textapp.ru

Dini, L., Peters, W., Liebwald, D., Schweighofer, E., Mommers, L., and Voermans, W., Cross-lingual legal information retrieval using a WordNet architecture, Proc. 10th Int. Conf. on Artificial Intelligence and Law, Bologna, 2005, New York: Association for Computing Machinery, 2005, pp. 163–167. https://doi.org/10.1145/1165485.1165510

Abramova, N.N. and Globus, E.I., Formirovanie mnogoyazychnykh slovarei i ikh ispol’zovanie pri kross-yazykovom poiske informatsii, Internet-matematika. Avtomaticheskaya obrabotka veb-dannykh (Internet Mathematics: Automated Processing of Web Data), 2005, pp. 18–37.

Curtoni, P., Dini, L., Tomaso, V., Mommers, L., Peters, W., Quaresma, P., Schweighofer, E., and Tiscornia, D., Semantic access to multilingual legal information, 1999.

Oard, D.W. and Hackett, P., Document translation for cross-language text retrieval at the University of Maryland, The 6th Text Retrieval Conf. (TREC-6), Voorchees, E M and Harman, D K, 1998.

McCarley, J.S., Should we translate the documents or the queries in cross-language information retrieval?, Proc. 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, College Park, Md., 1999, Stroudsburg, Pa.: Association for Computational Linguistics, 1999, pp. 208–214. https://doi.org/10.3115/1034678.1034716

Dumais, S., Letsche, T., Littman, M., and Landauer, T., Automatic cross-language retrieval using latent semantic indexing, AAAI Spring Symp. on Cross-Language Text and Speech Retrieval, Stanford Univ., 1997, pp. 18–24.

Chandar, S., Khapra, M.M., Larochelle, H., and Ravindran, B., Correlational neural networks, Neural Comput., 2014, vol. 28, no. 2, pp. 257–285. https://doi.org/10.1162/neco_a_00801MathSciNetCrossRef

Mueller, J. and Thyagarajan, A., Siamese recurrent architectures for learning sentence similarity, Proc. AAAI Conf. Artif. Intell., 2016, vol. 30, no. 1, pp. 2786–2792. https://doi.org/10.1609/aaai.v30i1.10350

Seki, K., On cross-lingual text similarity using neural translation models, J. Inf. Process., 2019, vol. 27, pp. 315–321. https://doi.org/10.2197/ipsjjip.27.315CrossRef

10.

Zhebel, V.V., Kreskin, A.D., and Sochenkov, I.V., Cross-lingual analysis of legal documents, Tr. Inst. Sist. Anal. Ross. Akad. Nauk, 2020, vol. 70, no. 1, pp. 24–29. https://doi.org/10.14357/20790279200103CrossRef

11.

Potthast, M., Barrón-Cedeño, A., Stein, B., and Rosso, P., Cross-language plagiarism detection, Lang. Resour. Eval., 2011, vol. 45, no. 1, pp. 45–62. https://doi.org/10.1007/s10579-009-9114-zCrossRef

12.

Sochenkov, I.V., Zubarev, D.V., and Tikhomirov, I.A., Exploratory patent search, Inf. Appl., 2018, no. 12, pp. 89–94. https://doi.org/10.14357/19922264180111

13.

Mikolov, T., Chen, K., Corrado, G., and Dean, J., Efficient estimation of word representations in vector space, ICLR Workshop, 2013.

14.

Rekabsaz, N., Lupu, M., Hanbury, A., and Zuccon, G., Generalizing translation models in the probabilistic relevance framework, Proc. 25th ACM Int. Conf. on Information and Knowledge Management, Indianapolis, Ind., 2016, New York: Association for Computing Machinery, 2016. https://doi.org/10.1145/2983323.2983833

15.

Robertson, S.E., Okapi at TREC-3.0, Proc. Third Text REtrieval Conf. (TREC 1994), Gaithersburg, Md.: 1994.

16.

Vulić, I. and Moens, M.F., Bilingual word embeddings from non-parallel document-aligned data applied to bilingual lexicon induction, Proc. 53rd Annu. Meeting of the Assoc. for Computational Linguistics and the 7th Int. Joint Conf. on Natural Language Processing, Stroudsburg, Pa.: Association for Computational Linguistics, 2015, vol. 2, pp. 719–725.

17.

Zubarev, D.V. and Sochenkov, I.V., Cross-lingual similar document retrieval methods, Tr. Inst. Sist. Programm. Ross. Akad. Nauk, 2019, vol. 31, no. 5, pp. 127–136. https://doi.org/10.15514/ispras-2019-31(5)-9CrossRef

18.

Tiedemann, J., Parallel data, tools and interfaces in OPUS, Proc. of the Language Resources and Evaluation (LREC), 2012, pp. 2214–2218.

19.

Artetxe, M. and Schwenk, H., Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond, Trans. Assoc. Comput. Linguist., 2019, vol. 7, pp. 597–610. https://doi.org/10.1162/tacl_a_00288CrossRef

20.

Johnson, J., Douze, M., and Jégou, H., Billion-scale similarity search with GPUs, IEEE Trans. Big Data, 2021, vol. 7, no. 3, pp. 535–547. https://doi.org/10.1109/tbdata.2019.2921572CrossRef

21.

Devyatkin, D., Pogorelskaya, Ya., Yadrintsev, V., and Sochenkov, I., Detection of missed links in large legal corpora, 2021 Ivannikov Memorial Workshop (IVMEM), Nizhny Novgorod, 2021, IEEE, 2021, pp. 23–27. https://doi.org/10.1109/ivmem53963.2021.00010

22.

Reimers, N. and Gurevych, I., Sentence-BERT: Sentence embeddings using Siamese BERT-networks, Proc. 2019 Conf. on Empirical Methods in Natural Language Processing and the 9th Int. Joint Conf. on Natural Language Processing (EMNLP-IJCNLP), Hong Kong: Association for Computational Linguistics, 2019, pp. 3982–3992. https://doi.org/10.18653/v1/d19-1410

Titel: Approaches to Cross-Language Retrieval of Similar Legal Documents Based on Machine Learning
verfasst von: V. V. Zhebel
D. A. Devyatkin
D. V. Zubarev
I. V. Sochenkov
Publikationsdatum: 01.12.2023
Verlag: Pleiades Publishing
Erschienen in: Scientific and Technical Information Processing / Ausgabe 5/2023
Print ISSN: 0147-6882
Elektronische ISSN: 1934-8118
DOI: https://doi.org/10.3103/S0147688223050167

Springer Professional

Abstract—

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 5/2023

Situational-Command Motion Control of an Autonomous Unmanned Aerial Vehicle Searching for Forest Fires in an Unstable Air Environment

Creation and Study of 3D Models for Digital Plant Phenotyping

Reflex or Reflection? The Oculomotor Behavior of the Companion Robot, Creating the Impression of Communicating with an Emotional Being

Efficient Bounded-Suboptimal Search for the Multiagent Pathfinding Problem

A Method for Deepfake Detection Using Convolutional Neural Networks

The IACPaaS Platform for Developing Systems Based on Ontologies: A Decade of Use

Premium Partner