5,773 research outputs found
Analysing the Resourcefulness of the Paragraph for Precedence Retrieval
Developing methods for extracting relevant legal information to aid legal
practitioners is an active research area. In this regard, research efforts are
being made by leveraging different kinds of information, such as meta-data,
citations, keywords, sentences, paragraphs, etc. Similar to any text document,
legal documents are composed of paragraphs. In this paper, we have analyzed the
resourcefulness of paragraph-level information in capturing similarity among
judgments for improving the performance of precedence retrieval. We found that
the paragraph-level methods could capture the similarity among the judgments
with only a few paragraph interactions and exhibit more discriminating power
over the baseline document-level method. Moreover, the comparison results on
two benchmark datasets for the precedence retrieval on the Indian supreme court
judgments task show that the paragraph-level methods exhibit comparable
performance with the state-of-the-art methodsComment: 5 pages , 3 figures, ICAIL 202
Findings from a literature review
Mentzingen, H., António, N., & Bação, F. (2023). Automation of legal precedents retrieval: Findings from a literature review. International Journal of Intelligent Systems, 2023, 1-22. [6660983]. https://doi.org/10.21203/rs.3.rs-2292464/v1, https://doi.org/10.21203/rs.3.rs-2292464/v2, https://doi.org/10.1155/2023/6660983---This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project-UIDB/04152/2020-Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.Judges frequently rely their reasoning on precedents. Courts must preserve uniformity in decisions while, depending on the legal system, previous cases compel rulings. The search for methods to accurately identify similar previous cases is not new and has been a vital input, for example, to case-based reasoning (CBR) methodologies. This literature review offers a comprehensive analysis of the advancements in automating the identification of legal precedents, primarily focusing on the paradigm shift from manual knowledge engineering to the incorporation of Artificial Intelligence (AI) technologies such as natural language processing (NLP) and machine learning (ML). While multiple approaches harnessing NLP and ML show promise, none has emerged as definitively superior, and further validation through statistically significant samples and expert-provided ground truth is imperative. Additionally, this review employs text-mining techniques to streamline the survey process, providing an accurate and holistic view of the current research landscape. By delineating extant research gaps and suggesting avenues for future exploration, this review serves as both a summation and a call for more targeted, empirical investigations.publishersversionpublishe
Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine
Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)
Comparative study on Judgment Text Classification for Transformer Based Models
This work involves the usage of various NLP models to predict the winner of a
particular judgment by the means of text extraction and summarization from a
judgment document. These documents are useful when it comes to legal
proceedings. One such advantage is that these can be used for citations and
precedence reference in Lawsuits and cases which makes a strong argument for
their case by the ones using it. When it comes to precedence, it is necessary
to refer to an ample number of documents in order to collect legal points with
respect to the case. However, reviewing these documents takes a long time to
analyze due to the complex word structure and the size of the document. This
work involves the comparative study of 6 different self-attention-based
transformer models and how they perform when they are being tweaked in 4
different activation functions. These models which are trained with 200
judgement contexts and their results are being judged based on different
benchmark parameters. These models finally have a confidence level up to 99%
while predicting the judgment. This can be used to get a particular judgment
document without spending too much time searching relevant cases and reading
them completely.Comment: 28 pages with 9 figure
U-CREAT: Unsupervised Case Retrieval using Events extrAcTion
The task of Prior Case Retrieval (PCR) in the legal domain is about
automatically citing relevant (based on facts and precedence) prior legal cases
in a given query case. To further promote research in PCR, in this paper, we
propose a new large benchmark (in English) for the PCR task: IL-PCR (Indian
Legal Prior Case Retrieval) corpus. Given the complex nature of case relevance
and the long size of legal documents, BM25 remains a strong baseline for
ranking the cited prior documents. In this work, we explore the role of events
in legal case retrieval and propose an unsupervised retrieval method-based
pipeline U-CREAT (Unsupervised Case Retrieval using Events Extraction). We find
that the proposed unsupervised retrieval method significantly increases
performance compared to BM25 and makes retrieval faster by a considerable
margin, making it applicable to real-time case retrieval systems. Our proposed
system is generic, we show that it generalizes across two different legal
systems (Indian and Canadian), and it shows state-of-the-art performance on the
benchmarks for both the legal systems (IL-PCR and COLIEE corpora).Comment: Accepted at ACL 2023, 15 pages (12 main + 3 Appendix
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