{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T06:52:08Z","timestamp":1773298328812,"version":"3.50.1"},"reference-count":26,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2021,7,27]]},"abstract":"\nPurpose<\/jats:title>\nIn the Indian judicial system, the court considers interpretations of similar previous judgments for the present case. An essential requirement of legal practitioners is to determine the most relevant judgments from an enormous amount of judgments for preparing supportive, beneficial and favorable arguments against the opponent. It urges a strong demand to develop a Legal Document Recommendation System (LDRS) to automate the process. In existing works, traditionally preprocessed judgment corpus is processed by Doc2Vec to learn semantically rich judgment embedding space (i.e. vector space). Here, vectors of semantically relevant judgments are in close proximity, as Doc2Vec can effectively capture semantic meanings. The enormous amount of judgments produces a huge noisy corpus and vocabulary which possesses a significant challenge: traditional preprocessing cannot fully eliminate noisy data from the corpus and due to this, the Doc2Vec demands huge memory and time to learn the judgment embedding. It also adversely affects the recommendation performance in terms of correctness. This paper aims to develop an effective and efficient LDRS to support civilians and the legal fraternity.<\/jats:p>\n<\/jats:sec>\n\nDesign\/methodology\/approach<\/jats:title>\nTo overcome previously mentioned challenges, this research proposes the LDRS that uses the proposed Generalized English and Indian Legal Dictionary (GEILD) which keeps the corpus of relevant dictionary words only and discards noisy elements. Accordingly, the proposed LDRS significantly reduces the corpus size, which can potentially improve the space and time efficiency of Doc2Vec.<\/jats:p>\n<\/jats:sec>\n\nFindings<\/jats:title>\nThe experimental results confirm that the proposed LDRS with GEILD yield superior performance in terms of accuracy, F1-Score, MCC-Score, with significant improvement in the space and time efficiency.<\/jats:p>\n<\/jats:sec>\n\nOriginality\/value<\/jats:title>\nThe proposed LDRS uses the customized domain-specific preprocessing and novel legal dictionary (i.e. GEILD) to precisely recommend the relevant judgments. The proposed LDRS can be incorporated with online legal search repositories\/engines to enrich their functionality.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-02-2021-0015","type":"journal-article","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T23:49:49Z","timestamp":1625528989000},"page":"187-203","source":"Crossref","is-referenced-by-count":8,"title":["Legal document recommendation system: a dictionary based approach"],"prefix":"10.1108","volume":"17","author":[{"given":"Jenish","family":"Dhanani","sequence":"first","affiliation":[]},{"given":"Rupa","family":"Mehta","sequence":"additional","affiliation":[]},{"given":"Dipti P.","family":"Rana","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,7,5]]},"reference":[{"key":"key2021082608211104700_ref001","first-page":"1","article-title":"Case vectors: spatial representations of the law using document embeddings","volume":"11","year":"2019","journal-title":"Law as Data"},{"key":"key2021082608211104700_ref002","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","year":"2003","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"key2021082608211104700_ref003","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10506-017-9193-x","article-title":"Data-centric and logic-based models for automated legal problem solving","volume":"25","year":"2017","journal-title":"Artificial Intelligence and Law"},{"issue":"2","key":"key2021082608211104700_ref004","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10506-018-9238-9","article-title":"Deep learning in law: early adaptation and legal word embeddings trained on large corpora","volume":"27","year":"2019","journal-title":"Artificial Intelligence and Law"},{"issue":"1","key":"key2021082608211104700_ref005","first-page":"1","article-title":"Ten quick tips for machine learning in computational biology","volume":"10","year":"2017","journal-title":"BioData Mining"},{"key":"key2021082608211104700_ref006","first-page":"855","article-title":"node2vec: Scalable feature learning for networks","volume-title":"Proceeding of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","year":"2016"},{"key":"key2021082608211104700_ref007","first-page":"1","article-title":"Design of a meta search system for legal domain","year":"2017"},{"issue":"3","key":"key2021082608211104700_ref008","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s10462-017-9566-2","article-title":"Text summarization from legal documents: a survey","volume":"51","year":"2019","journal-title":"Artificial Intelligence Review"},{"issue":"1","key":"key2021082608211104700_ref009","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1002\/asi.5090140103","article-title":"Bibliographic coupling between scientific papers","volume":"14","year":"1963","journal-title":"American Documentation"},{"issue":"2","key":"key2021082608211104700_ref010","first-page":"243","article-title":"Network analysis in the legal domain: a complex model for European union legal sources","volume":"6","year":"2017","journal-title":"Journal of Complex Networks"},{"key":"key2021082608211104700_ref011","first-page":"103","article-title":"Finding similar legal judgements under common law system","year":"2013"},{"key":"key2021082608211104700_ref012","first-page":"1","article-title":"Similarity analysis of legal judgments","year":"2011"},{"key":"key2021082608211104700_ref013","first-page":"1188","article-title":"Distributed representations of sentences and documents","year":"2014"},{"issue":"2","key":"key2021082608211104700_ref014","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s10506-018-9224-2","article-title":"Bending the law: geometric tools for quantifying influence in the multinetwork of legal opinions","volume":"26","year":"2018","journal-title":"Artificial Intelligence and Law"},{"issue":"5","key":"key2021082608211104700_ref015","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1177\/0165551517722741","article-title":"A two-phase sentiment analysis approach for judgement prediction","volume":"44","year":"2018","journal-title":"Journal of Information Science"},{"issue":"10","key":"key2021082608211104700_ref016","first-page":"883","article-title":"Exploratory analysis of legal case citation data using node embedding","volume":"13","year":"2019","journal-title":"ICIC Express Letters"},{"key":"key2021082608211104700_ref017","first-page":"1","article-title":"Measuring similarity among legal court case documents","volume-title":"Proceedings of the Annual ACM India Compute Conference","year":"2017"},{"key":"key2021082608211104700_ref018","first-page":"404","article-title":"Textrank: Bringing order into text","year":"2004"},{"key":"key2021082608211104700_ref019","first-page":"1","article-title":"Efficient estimation of word representations in vector space","year":"2013"},{"key":"key2021082608211104700_ref020","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","year":"2013","journal-title":"Advances in Neural Information Processing Systems"},{"key":"key2021082608211104700_ref021","first-page":"68","article-title":"Legal information retrieval using topic clustering and neural networks","year":"2017","journal-title":"Proceeding of the COLIEE@ ICAIL"},{"key":"key2021082608211104700_ref022","first-page":"449","article-title":"Text and citations based cluster analysis of legal judgments","year":"2015"},{"key":"key2021082608211104700_ref023","article-title":"Deep ensemble learning for legal query understanding","year":"2018"},{"issue":"4","key":"key2021082608211104700_ref024","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1002\/asi.4630240406","article-title":"Co-citation in the scientific literature: a new measure of the relationship between two documents","volume":"24","year":"1973","journal-title":"Journal of the American Society for Information Science"},{"key":"key2021082608211104700_ref025","first-page":"160","article-title":"Legal document retrieval using document vector embeddings and deep learning","volume-title":"proceeding of the Science and Information Conference","year":"2018"},{"key":"key2021082608211104700_ref026","first-page":"127","article-title":"Lexical semantics and expert legal knowledge toward the identification of legal case factors","volume":"10","year":"2010","journal-title":"Proceeding of the JURIX"}],"container-title":["International Journal of Web Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-02-2021-0015\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-02-2021-0015\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:23:44Z","timestamp":1753395824000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijwis\/article\/17\/3\/187-203\/161007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,5]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,7,5]]},"published-print":{"date-parts":[[2021,7,27]]}},"alternative-id":["10.1108\/IJWIS-02-2021-0015"],"URL":"https:\/\/doi.org\/10.1108\/ijwis-02-2021-0015","relation":{},"ISSN":["1744-0084","1744-0084"],"issn-type":[{"value":"1744-0084","type":"print"},{"value":"1744-0084","type":"print"}],"subject":[],"published":{"date-parts":[[2021,7,5]]}}}