HLDC: Hindi Legal Documents Corpus

Kapoor, Arnav; Dhawan, Mudit; Goel, Anmol; Arjun, T H; Bhatnagar, Akshala; Agrawal, Vibhu; Agrawal, Amul; Bhattacharya, Arnab; Kumaraguru, Ponnurangam; Modi, Ashutosh

doi:10.18653/v1/2022.findings-acl.278

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…SwissJudgment [27] German Pleas Facts -Plea judgments Link Italian French Sulea et al [14] Facts Others -CanAppeal [41] English Facts USClassActions [42] Pleas Link JUSTICE [28] Pleas Facts -BStricks_LDC [43] Facts Others Link ILDC [44] Fact snippets Link ACI [45] Facts Charges Link COLIEETask1 [46] Facts Precedents Link AILA [47] Facts echr [13] Facts Articles -Link ECHR-CASES [26] Link ECtHR [48] Link PhilCases [49] -Plea judgments -SCDB [50], [51] Facts Plea judgments Link HLDC [52] Hindi Facts Plea judgments Link TSCC [53] Thai -Link MLMN [54] Chinese Facts Articles Prison terms Link DPAM [55] Facts Articles -Link CAIL2018 [25] Charges Prison terms Link TOPJUDGE-CJO [30] -TOPJUDGE-PKU [30] TOPJUDGE-CAIL [30] CAIL-Long [56] Link FLA [17] Charges -RACP [57] Fact snippets MAMD [58] -Court-View-Gen [34] Court View Link AC-NLG [35] Facts Pleas Link CPTP [59] Charges -Prison terms Link Criminal-S [60] Charge attributes Attributes prediction Charges Link Criminal-M [60] Criminal-L [60] QAjudge [61] Elements questions Articles Elements answers -Auto-Judge [62] Pleas Facts Articles -Plea judgments LJP-MSJudge [63] Debate data Plea judgments Link law-turk [64] Turkish Facts Plea judgments Link BrCases [65] Portuguese Homicides …”

Section: Datasetmentioning

confidence: 99%

“…CanAppeal [41] BrCases [65] Homicides [66] Corruption [66] JUSTICE [28] BStricks_LDC [43] Sulea et al [14] Auto-Judge [62] CAIL2018 Articles Charges Prison terms TOPJUDGE-CJO,TOPJUDGE-PKU,TOPJUDGE-CAI [30] Articles Charges Prison terms QAjudge-CJO,QAjudge-PKU,QAjudge-CAIL [61] Articles Charges Criminal-S,Criminal-M,Criminal-L [60] ILDC [44] Plea judgments Fact snippets JS ROUGE BLEU Meteor DPAM [55] Articles MaF MaP MaR SwissJudgment [27] Plea judgments MaF/MiF TSCC [53] Articles CAIL-Long [56] Charges Prison terms LD QAjudge-CJO,QAjudge-PKU,QAjudge-CAIL [61] Elements answers Acc MaF HLDC [52] Plea judgments MiF law-turk [64] BACC MaF COLIEETask1 Precedents MiF MiP MiR FLA [17] Charges MaF/MiF MaP/MiP MaR/MiR LJP-MSJudge [63] Plea judgments MaP MaR Court-View-Gen [34] Court View ROUGE BLEU AC-NLG [87] BERT SCORE ROUGE CPTP [59] Prison terms S EM Acc@p BrCAD-5 [67] Plea judgments MCC intersection divided by the size of the union of the two sets, which is follows:…”

Section: Accmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges

Cui,

Shen,

Wen

2023

IEEE Access

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Legal judgment prediction (LJP) applies Natural Language Processing (NLP) techniques to predict judgment results based on fact descriptions automatically. The present work addresses the growing interest in the application of NLP techniques to the task of LJP. Despite the current performance gap between machines and humans, promising results have been achieved in a variety of benchmark datasets, owing to recent advances in NLP research and the availability of large-scale public datasets. To provide a comprehensive survey of existing LJP tasks, datasets, models, and evaluations, this study presents the following contributions: (1) an analysis of 43 LJP datasets constructed in 9 different languages, together with a classification method of LJP based on three different attributes; (2) a summary of 16 evaluation metrics categorized into 4 different types to evaluate the performance of LJP models for different outputs;(3) a review of 8 legal-domain pretrained models in 4 languages, highlighting four major research directions for LJP; (4) state-of-the-art results for 11 representative datasets from different court cases and an in-depth discussion of the open challenges in this area. This study aims to provide a comprehensive review for NLP researchers and legal professionals to understand the advances in LJP over the past years, and to facilitate further joint efforts towards improving the performance of LJP models.

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Section: Datasetmentioning

confidence: 99%

Section: Accmentioning

confidence: 99%

A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges

Cui,

Shen,

Wen

2023

IEEE Access

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“…This paper explores the use of machine learning techniques to predict the outcomes of cases in the European Court of Human Rights. Hindi legal Document Corpus [7] is a dataset provided for the purpose of Bail prediction in Hindi Language. The paper aims at predicting whether the appellant would get bail or not as a binary classification task by using a battery of models.…”

Section: Related Workmentioning

confidence: 99%

Fact-based Court Judgment Prediction

Nigam,

Deroy

2023

Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation

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This extended abstract extends the research presented in "ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation" [10], focusing on fact-based judgment prediction within the context of Indian legal documents. We introduce two distinct problem variations: one based solely on facts, and another combining facts with rulings from lower courts (RLC). Our research aims to enhance early-phase case outcome prediction, offering significant benefits to legal professionals and the general public. The results, however, indicated a performance decline compared to the original ILDC for CJPE study, even after implementing various weightage schemes in our DELSumm algorithm. Additionally, using only facts for legal judgment prediction with different transformer models yielded results inferior to the state-of-the-art outcomes reported in the "ILDC for CJPE" study.

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“…Corpora of legal texts are increasingly available and accessible. This is especially true of legislation (Chalkidis et al, 2019;Váradi et al, 2020), but it also applies to court judgments (Grover et al, 2004;Poudyal et al, 2020;Feng et al, 2022;Kapoor et al, 2022) and other types of legal texts (e.g. con-tracts, Funaki et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Annotators-in-the-loop: Testing a Novel Annotation Procedure on Italian Case Law

Zanoli¹,

Barbini²,

Riva³

et al. 2023

Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)

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The availability of annotated legal corpora is crucial for a number of tasks, such as legal search, legal information retrieval, and predictive justice. Annotation is mostly assumed to be a straightforward task: as long as the annotation scheme is well defined and the guidelines are clear, annotators are expected to agree on the labels. This is not always the case, especially in legal annotation, which can be extremely difficult even for expert annotators. We propose a legal annotation procedure that takes into account annotator certainty and improves it through negotiation. We also collect annotator feedback and show that our approach contributes to a positive annotation environment. Our work invites reflection on often neglected ethical concerns regarding legal annotation.

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HLDC: Hindi Legal Documents Corpus

Cited by 8 publications

References 8 publications

A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges

A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges

Fact-based Court Judgment Prediction

Annotators-in-the-loop: Testing a Novel Annotation Procedure on Italian Case Law

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