Approaches to Cross-Language Retrieval of Similar Legal Documents Based on Machine Learning
— 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 doc...
Gespeichert in:
| Veröffentlicht in: | Scientific and technical information processing 2023-12, Vol.50 (5), p.494-499 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 499 |
|---|---|
| container_issue | 5 |
| container_start_page | 494 |
| container_title | Scientific and technical information processing |
| container_volume | 50 |
| creator | Zhebel, V. V. Devyatkin, D. A. Zubarev, D. V. Sochenkov, I. V. |
| description | —
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. |
| doi_str_mv | 10.3103/S0147688223050167 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2937810606</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2937810606</sourcerecordid><originalsourceid>FETCH-LOGICAL-c268t-fda6233d3c6ed0128bf57be49f0afd0a4c3999e01dbebc505a5cce200b9b2bbe3</originalsourceid><addsrcrecordid>eNp1kE9LxDAQxYMouK5-AG8Bz9VJ0qbNUde_UBFcPXkoSTqtXXbTNWkFv70pK3gQTwMzv_eG9wg5ZXAuGIiLJbA0l0XBuYAMmMz3yIwpkSYFY8U-mU3nZLofkqMQVgCZ5KmakbfL7db32r5joENPF74PISm1a0fdIn3GwXf4qde0b-iy23Rr7WmJbVxc93bcoBsCvdIBa9o7-hhtOocR0N51rj0mB41eBzz5mXPyenvzsrhPyqe7h8VlmVguiyFpai25ELWwEmtgvDBNlhtMVQO6qUGnViilEFht0NgMMp1ZixzAKMONQTEnZzvfmORjxDBUq370Lr6suBJ5wUCCjBTbUXbK6LGptr7baP9VMaimDqs_HUYN32lCZF2L_tf5f9E3nDlzxA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2937810606</pqid></control><display><type>article</type><title>Approaches to Cross-Language Retrieval of Similar Legal Documents Based on Machine Learning</title><source>SpringerNature Journals</source><creator>Zhebel, V. V. ; Devyatkin, D. A. ; Zubarev, D. V. ; Sochenkov, I. V.</creator><creatorcontrib>Zhebel, V. V. ; Devyatkin, D. A. ; Zubarev, D. V. ; Sochenkov, I. V.</creatorcontrib><description>—
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.</description><identifier>ISSN: 0147-6882</identifier><identifier>EISSN: 1934-8118</identifier><identifier>DOI: 10.3103/S0147688223050167</identifier><language>eng</language><publisher>Moscow: Pleiades Publishing</publisher><subject>Computer Science ; Computer Systems Organization and Communication Networks ; Digital systems ; Documents ; Information retrieval ; Languages ; Legislation ; Machine learning ; Semantics ; Translations</subject><ispartof>Scientific and technical information processing, 2023-12, Vol.50 (5), p.494-499</ispartof><rights>Allerton Press, Inc. 2023. ISSN 0147-6882, Scientific and Technical Information Processing, 2023, Vol. 50, No. 5, pp. 494–499. © Allerton Press, Inc., 2023. Russian Text © The Author(s), 2022, published in Iskusstvennyi Intellekt i Prinyatie Reshenii, 2022, No. 2, pp. 27–35.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c268t-fda6233d3c6ed0128bf57be49f0afd0a4c3999e01dbebc505a5cce200b9b2bbe3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.3103/S0147688223050167$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.3103/S0147688223050167$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,782,786,27931,27932,41495,42564,51326</link.rule.ids></links><search><creatorcontrib>Zhebel, V. V.</creatorcontrib><creatorcontrib>Devyatkin, D. A.</creatorcontrib><creatorcontrib>Zubarev, D. V.</creatorcontrib><creatorcontrib>Sochenkov, I. V.</creatorcontrib><title>Approaches to Cross-Language Retrieval of Similar Legal Documents Based on Machine Learning</title><title>Scientific and technical information processing</title><addtitle>Sci. Tech. Inf. Proc</addtitle><description>—
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.</description><subject>Computer Science</subject><subject>Computer Systems Organization and Communication Networks</subject><subject>Digital systems</subject><subject>Documents</subject><subject>Information retrieval</subject><subject>Languages</subject><subject>Legislation</subject><subject>Machine learning</subject><subject>Semantics</subject><subject>Translations</subject><issn>0147-6882</issn><issn>1934-8118</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp1kE9LxDAQxYMouK5-AG8Bz9VJ0qbNUde_UBFcPXkoSTqtXXbTNWkFv70pK3gQTwMzv_eG9wg5ZXAuGIiLJbA0l0XBuYAMmMz3yIwpkSYFY8U-mU3nZLofkqMQVgCZ5KmakbfL7db32r5joENPF74PISm1a0fdIn3GwXf4qde0b-iy23Rr7WmJbVxc93bcoBsCvdIBa9o7-hhtOocR0N51rj0mB41eBzz5mXPyenvzsrhPyqe7h8VlmVguiyFpai25ELWwEmtgvDBNlhtMVQO6qUGnViilEFht0NgMMp1ZixzAKMONQTEnZzvfmORjxDBUq370Lr6suBJ5wUCCjBTbUXbK6LGptr7baP9VMaimDqs_HUYN32lCZF2L_tf5f9E3nDlzxA</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Zhebel, V. V.</creator><creator>Devyatkin, D. A.</creator><creator>Zubarev, D. V.</creator><creator>Sochenkov, I. V.</creator><general>Pleiades Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20231201</creationdate><title>Approaches to Cross-Language Retrieval of Similar Legal Documents Based on Machine Learning</title><author>Zhebel, V. V. ; Devyatkin, D. A. ; Zubarev, D. V. ; Sochenkov, I. V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c268t-fda6233d3c6ed0128bf57be49f0afd0a4c3999e01dbebc505a5cce200b9b2bbe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science</topic><topic>Computer Systems Organization and Communication Networks</topic><topic>Digital systems</topic><topic>Documents</topic><topic>Information retrieval</topic><topic>Languages</topic><topic>Legislation</topic><topic>Machine learning</topic><topic>Semantics</topic><topic>Translations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhebel, V. V.</creatorcontrib><creatorcontrib>Devyatkin, D. A.</creatorcontrib><creatorcontrib>Zubarev, D. V.</creatorcontrib><creatorcontrib>Sochenkov, I. V.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Scientific and technical information processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhebel, V. V.</au><au>Devyatkin, D. A.</au><au>Zubarev, D. V.</au><au>Sochenkov, I. V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Approaches to Cross-Language Retrieval of Similar Legal Documents Based on Machine Learning</atitle><jtitle>Scientific and technical information processing</jtitle><stitle>Sci. Tech. Inf. Proc</stitle><date>2023-12-01</date><risdate>2023</risdate><volume>50</volume><issue>5</issue><spage>494</spage><epage>499</epage><pages>494-499</pages><issn>0147-6882</issn><eissn>1934-8118</eissn><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.</abstract><cop>Moscow</cop><pub>Pleiades Publishing</pub><doi>10.3103/S0147688223050167</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0147-6882 |
| ispartof | Scientific and technical information processing, 2023-12, Vol.50 (5), p.494-499 |
| issn | 0147-6882 1934-8118 |
| language | eng |
| recordid | cdi_proquest_journals_2937810606 |
| source | SpringerNature Journals |
| subjects | Computer Science Computer Systems Organization and Communication Networks Digital systems Documents Information retrieval Languages Legislation Machine learning Semantics Translations |
| title | Approaches to Cross-Language Retrieval of Similar Legal Documents Based on Machine Learning |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-04T01%3A14%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Approaches%20to%20Cross-Language%20Retrieval%20of%20Similar%20Legal%20Documents%20Based%20on%20Machine%20Learning&rft.jtitle=Scientific%20and%20technical%20information%20processing&rft.au=Zhebel,%20V.%20V.&rft.date=2023-12-01&rft.volume=50&rft.issue=5&rft.spage=494&rft.epage=499&rft.pages=494-499&rft.issn=0147-6882&rft.eissn=1934-8118&rft_id=info:doi/10.3103/S0147688223050167&rft_dat=%3Cproquest_cross%3E2937810606%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2937810606&rft_id=info:pmid/&rfr_iscdi=true |