IITP at AILA 2019: System Report for Artificial Intelligence for Legal Assistance Shared Task

• URL: http://arxiv.org/abs/2105.11347v1
• Date: Mon, 24 May 2021 15:31:24 GMT
• Title: IITP at AILA 2019: System Report for Artificial Intelligence for Legal Assistance Shared Task
• Authors: Baban Gain, Dibyanayan Bandyopadhyay, Arkadipta De, Tanik Saikh, Asif Ekbal
• Abstract summary: We present a description of our systems as a part of our participation in the shared task namely Artificial Intelligence for Legal Assistance (AILA) The outcomes of this track would be helpful for the automation of the working process of the Indian Judiciary System. This kind of track also opens the path of research of Natural Language Processing (NLP) in the judicial domain.
• Score: 22.01644160437953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this article, we present a description of our systems as a part of our participation in the shared task namely Artificial Intelligence for Legal Assistance (AILA 2019). This is an integral event of Forum for Information Retrieval Evaluation-2019. The outcomes of this track would be helpful for the automation of the working process of the Indian Judiciary System. The manual working procedures and documentation at any level (from lower to higher court) of the judiciary system are very complex in nature. The systems produced as a part of this track would assist the law practitioners. It would be helpful for common men too. This kind of track also opens the path of research of Natural Language Processing (NLP) in the judicial domain. This track defined two problems such as Task 1: Identifying relevant prior cases for a given situation and Task 2: Identifying the most relevant statutes for a given situation. We tackled both of them. Our proposed approaches are based on BM25 and Doc2Vec. As per the results declared by the task organizers, we are in 3rd and a modest position in Task 1 and Task 2 respectively.

Related papers

• A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences [76.73731245899454]
  We propose a transparent law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience. Inspired by this schema, we introduce the challenging task, which takes a textual case description and outputs a hierarchical structure justifying the final decision. This benchmark paves the way for transparent and accountable AI-assisted law reasoning in the Intelligent Court''
  arXiv  Detail & Related papers  (2025-03-02T10:26:54Z)
• AnnoCaseLaw: A Richly-Annotated Dataset For Benchmarking Explainable Legal Judgment Prediction [56.797874973414636]
  AnnoCaseLaw is a first-of-its-kind dataset of 471 meticulously annotated U.S. Appeals Court negligence cases. Our dataset lays the groundwork for more human-aligned, explainable Legal Judgment Prediction models. Results demonstrate that LJP remains a formidable task, with application of legal precedent proving particularly difficult.
  arXiv  Detail & Related papers  (2025-02-28T19:14:48Z)
• The Foundations of Computational Management: A Systematic Approach to Task Automation for the Integration of Artificial Intelligence into Existing Workflows [55.2480439325792]
  This article introduces Computational Management, a systematic approach to task automation. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
  arXiv  Detail & Related papers  (2024-02-07T01:45:14Z)
• Report of the 1st Workshop on Generative AI and Law [78.62063815165968]
  This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw) A cross-disciplinary group of practitioners and scholars from computer science and law convened to discuss the technical, doctrinal, and policy challenges presented by law for Generative AI.
  arXiv  Detail & Related papers  (2023-11-11T04:13:37Z)
• SemEval 2023 Task 6: LegalEval - Understanding Legal Texts [2.172613863157655]
  There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document, Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for
  arXiv  Detail & Related papers  (2023-04-19T10:28:32Z)
• Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022 [2.242125769416219]
  We introduce efficient deep learning-based methods for legal document processing in the Automated Legal Question Answering Competition (ALQAC 2022) Our method is based on the XLM-RoBERTa model that is pre-trained from a large amount of unlabeled corpus before fine-tuning to the specific tasks. The experimental results showed that our method works well in legal retrieval information tasks with limited labeled data.
  arXiv  Detail & Related papers  (2022-11-04T00:50:20Z)
• An Uncommon Task: Participatory Design in Legal AI [64.54460979588075]
  We examine a notable yet understudied AI design process in the legal domain that took place over a decade ago. We show how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers.
  arXiv  Detail & Related papers  (2022-03-08T15:46:52Z)
• Information Extraction through AI techniques: The KIDs use case at CONSOB [114.00746142672475]
  We focus on Information Extraction from documents describing financial instruments. We discuss how we automate this task, via both rule-based and machine learning-based methods.
  arXiv  Detail & Related papers  (2022-01-29T20:05:28Z)
• JNLP Team: Deep Learning Approaches for Legal Processing Tasks in COLIEE 2021 [1.8700700550095686]
  COLIEE is an annual competition in automatic computerized legal text processing. In this article, we report our methods and experimental results in using deep learning in legal document processing.
  arXiv  Detail & Related papers  (2021-06-25T03:31:12Z)
• IITP in COLIEE@ICAIL 2019: Legal Information Retrieval usingBM25 and BERT [23.62025029923504]
  The paper presents our working note on the experiments carried out as a part of our participation in the shared task. We make use of different Information Retrieval(IR) and deep learning based approaches to tackle these problems.
  arXiv  Detail & Related papers  (2021-04-17T22:28:15Z)
• THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment [41.51705651274111]
  We present our methodologies for tackling the challenges of legal case retrieval and entailment. We participated in the two case law tasks, i.e., the legal case retrieval task and the legal case entailment task. In both tasks, we employed the neural models for semantic understanding and the traditional retrieval models for exact matching.
  arXiv  Detail & Related papers  (2020-12-24T04:59:45Z)
• How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence [81.04070052740596]
  Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. This paper introduces the history, the current state, and the future directions of research in LegalAI.
  arXiv  Detail & Related papers  (2020-04-25T14:45:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.