Complexity Analysis of Legal Documents

被引:0
|
作者
Ramaswamy, Sankar [1 ]
Sreelekshmi, R. [1 ]
Veena, G. [1 ]
机构
[1] Amrita Vishwa Vidyapeetham, Dept Comp Sci & Applicat, Amrita Sch Comp, Amritapuri, India
来源
ARTIFICIAL INTELLIGENCE: THEORY AND APPLICATIONS, VOL 1, AITA 2023 | 2024年 / 843卷
关键词
Named entity recognition; Summarising; Natural language processing; Indian legal system; Information extraction;
D O I
10.1007/978-981-99-8476-3_12
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Legal documents are considered one of the most difficult types of documents to write and interpret due to the fact that they use legal jargon and follow a specific legal convention. For a legal practitioner, they require documents, mainly judgements, for the proceedings in their client's case. We evaluated the approach of Named Entity Recognition on a large corpus of legal judgements and found 14 entities, such as case number, name of judge, name of court, statute, provision, etc. Through this, we found that one cannot comprehend the document completely as it is written in a complex manner. We also conduct a complexity analysis of the legal documents using a variety of linguistic features, including sentence length, word frequency, and syntactic complexity. Considering the presence of legal jargon, we defined a method to analyse the complexity on these legal documents and a suitable score to understand them. Given that there are no specific ways for defining the complexity of legal documents, we performed human evaluation and achieved the best value for the recommended approaches with the assistance of legal experts.
引用
收藏
页码:141 / 154
页数:14
相关论文
共 50 条
  • [21] On the Assessment of Deep Learning Models for Named Entity Recognition of Brazilian Legal Documents
    Albuquerque, Hidelberg O.
    Souza, Ellen
    Oliveira, Adriano L. I.
    Macedo, David
    Zanchettin, Cleber
    Vitorio, Douglas
    da Silva, Nadia F. F.
    de Carvalho, Andre C. P. L. F.
    PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2023, PT II, 2023, 14116 : 93 - 104
  • [22] Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents
    Pires, Ramon
    de Souza, Fabio C.
    Rosa, Guilherme
    Lotufo, Roberto A.
    Nogueira, Rodrigo
    DOCUMENT ANALYSIS SYSTEMS, DAS 2022, 2022, 13237 : 83 - 95
  • [23] Use of Artificial Intelligence to Analyse Risk in Legal Documents for a Better Decision Support
    Chakrabarti, Dipankar
    Patodia, Neelam
    Bhattacharya, Udayan
    Mitra, Indranil
    Roy, Satyaki
    Mandi, Jayanta
    Roy, Nandini
    Nandy, Prasun
    PROCEEDINGS OF TENCON 2018 - 2018 IEEE REGION 10 CONFERENCE, 2018, : 0683 - 0688
  • [24] A RoBERTa-GlobalPointer-Based Method for Named Entity Recognition of Legal Documents
    Zhang, Xinrui
    Luo, Xudong
    Wu, Jiaye
    2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, 2023,
  • [25] Empowering LLMs for Long-Text Information Extraction in Chinese Legal Documents
    Shen, Chenchen
    Ji, Chengwei
    Yue, Shengbin
    Shen, Xiaoyu
    Song, Yun
    Huang, Xuanjing
    Wei, Zhongyu
    NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, PT I, NLPCC 2024, 2025, 15359 : 457 - 469
  • [26] GarNLP: A Natural Language Processing Pipeline for Garnishment Documents
    Ilaria Bordino
    Andrea Ferretti
    Francesco Gullo
    Stefano Pascolutti
    Information Systems Frontiers, 2021, 23 : 101 - 114
  • [27] GarNLP: A Natural Language Processing Pipeline for Garnishment Documents
    Bordino, Ilaria
    Ferretti, Andrea
    Gullo, Francesco
    Pascolutti, Stefano
    INFORMATION SYSTEMS FRONTIERS, 2021, 23 (01) : 101 - 114
  • [28] Extracting Complex Named Entities in Legal Documents via Weakly Supervised Object Detection
    Yang, Hsiu-Wei
    Agrawal, Abhinav
    PROCEEDINGS OF THE 46TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, SIGIR 2023, 2023, : 3349 - 3353
  • [29] An Information Extraction Framework for Legal Documents: a Case Study of Thai Supreme Court Verdicts
    Kowsrihawat, Kankawin
    Vateekul, Peerapon
    PROCEEDINGS OF THE 2015 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE), 2015, : 275 - 280
  • [30] Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents
    Georgescu, Tiberiu-Marian
    SYMMETRY-BASEL, 2020, 12 (03):