Legal document similarity: a multi-criteria decision-making perspective

Abstract

The vast volume of documents available in legal databases demands effective information retrieval approaches which take into consideration the intricacies of the legal domain. Relevant document retrieval is the backbone of the legal domain. The concept of relevance in the legal domain is very complex and multi-faceted. In this work, we propose a novel approach of concept based similarity estimation among court judgments. We use a graph-based method, to identify prominent concepts present in a judgment and extract sentences representative of these concepts. The sentences and concepts so mined are used to express/visualize likeness among concepts between a pair of documents from different perspectives. We also propose to aggregate the different levels of matching so obtained into one measure quantifying the level of similarity between a judgment pair. We employ the ordered weighted average (OWA) family of aggregation operators for obtaining the similarity value. The experimental results suggest that the proposed approach of concept based similarity is effective in the extraction of relevant legal documents and performs better than other competing techniques. Additionally, the proposed two-level abstraction of similarity enables informative visualization for deeper insights into case relevance.

Revisiting the challenges and surveys in text similarity matching and detection methods

“…Relevant document retrieval is essential for some domains. An example of this application is legal information retrieval to find legal document similarity [32], [33]. The source of legal information is derived from various documents, such as court transcripts, legislation documents, verdicts, and judgment generated from the legal process.…”

“…Another application in the medical domain is the development of automatic Q&A systems using text matching [23]. The syntax and meaning of medical words are more complex, with many complicated Fields of Applications Plagiarism detection system ( [12], [18], [24], [28], [29], [31]) Topic identification ( [32], [33], [34]) Medical domain ( [23], [35], [36])…”

Recognition of Chinese Legal Elements Based on Transfer Learning and Semantic Relevance

“…application of the law [5]. In the process of building a "smart court," the element identification task plays an important role, aiming to extract key elements from legal documents automatically.…”

Subjective Answers Evaluation Using Machine Learning and Natural Language Processing

“…Wagh et al [35] proposed a multi-criteria decision-making perspective to find the Similarity between legal documents. The work included using Artificial Intelligence and aggregation techniques such as ordered weighted average (OWA) for obtaining the similarity value between different documents.…”