Legal Citation Recommendation System
Legal Citation Recommendation System |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-9 |
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| Year of Publication : 2025 | ||
| Author : Sonali Antad, Viomesh Singh, Vaishali Rajput, Onkar Waghmode, Shripad Wattamwar, Atharva Wagh, Aditya Zite | ||
| DOI : 10.14445/22315381/IJETT-V73I9P119 | ||
How to Cite?
Sonali Antad, Viomesh Singh, Vaishali Rajput, Onkar Waghmode, Shripad Wattamwar, Atharva Wagh, Aditya Zite,"Legal Citation Recommendation System", International Journal of Engineering Trends and Technology, vol. 73, no. 9, pp.207-216, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I9P119
Abstract
Citations in the legal field relate to earlier rulings cited in support of the current case. Attorneys use citations to create compelling arguments and ensure uniformity in rulings. However, the process is difficult and time-consuming for attorneys because it is like needle-hunting to identify pertinent quotations from many judgments. This procedure is greatly improved by Legal Citation Recommendation Systems (LCRS), which rapidly find the most relevant citations. LCRS typically evaluates the pairwise similarity between judgments; however, problems occur because of the judgments' uneven lengths and information overload. The similarity score is directly impacted by these difficulties, which also result in additional noise, semantic dilution effects, size-induced similarity degradation, and dimensional inconsistencies. Research suggests a technique to deal with similarity deterioration in which assessments are divided into different pieces using regular expressions. The sections are chosen after consulting subject-matter experts. Because a judgment has several portions, summarization and semantic chunking are used to construct sections of the right size while addressing dimensional inconsistencies and noise. This method concentrates on discovering similarities between matching portions rather than similarities between full judgments. A more accurate similarity estimate is then obtained by calculating the average of these section-wise similarities. The preference or precedence of parts based on user requirements is also incorporated into this strategy. The LCRS becomes more dynamic and more in line with user needs when parts are given weighted similarity values.
Keywords
Size-induced similarity degradation, Semantic dilution, Legal bert, Regex, Semantic chunking, FAISS vector space.
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