scholarly journals An overview of information extraction techniques for legal document analysis and processing

Author(s):  
Ashwini V. Zadgaonkar ◽  
Avinash J. Agrawal

<span>In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing.</span>

Author(s):  
Jenish Dhanani ◽  
Rupa G. Mehta ◽  
Dipti P. Rana ◽  
Rahul Lad ◽  
Amogh Agrawal ◽  
...  

Recently, legal information retrieval has emerged as an essential practice for the legal fraternity. In the legal domain, judgment is a specific kind of legal document, which discusses case-related information and the verdict of a court case. In the common law system, the legal professionals exploit relevant judgments to prepare arguments. Hence, an automated system is a vital demand to identify similar judgments effectively. The judgments can be broadly categorized into civil and criminal cases, where judgments with similar case matters can have strong relevance compared to judgments with different case matters. In similar judgment identification, categorized judgments can significantly prune search space by restrictive search within a specific case category. So, this chapter provides a novel methodology that classifies Indian judgments in either of the case matter. Crucial challenges like imbalance and intrinsic characteristics of legal data are also highlighted specific to similarity analysis of Indian judgments, which can be a motivating aspect to the research community.


2019 ◽  
Vol 8 (4) ◽  
pp. 9552-9556

In the web industry 4.0, big data are playing a key role in the organizations of their digital transformation journey. Data has also changed intensely in recent years, in volume, variety and velocity. Its fast development attributed to the extensive digitization of business progressions globally. In simple term, data has turn into the new currency of business and its further quick growth will be key transformation and growth of organizations globally. Vast amount of online information, available in healthcare, social media websites, e-commerce web pages, e-books, legal domain, e-news, etc. has made text processing a vital extent of research. The paper starts with introduction about the evolution of web industry 4.0 and digitalization. Followed by the introduction, the paper discusses about big data and text summarization techniques. Further, it describes the literature of text mining that have taken in the recent years. The main objective of this paper is to discuss about the big data text summarization issues and challenges. The paper starts with general introduction of big data and text mining and text summarization. Further it describes recent advances in big data text summarization, and then delve into extraction and abstraction-based text summarization. Finally, the paper concludes with some future research directions.


2020 ◽  
Vol 7 (3) ◽  
pp. 471-494
Author(s):  
Katsumi NITTA ◽  
Ken SATOH

AbstractArtificial intelligence (AI) and law is an AI research area that has a history spanning more than 50 years. In the early stages, several legal-expert systems were developed. Legal-expert systems are tools designed to realize fair judgments in court. In addition to this research, as information and communication technologies and AI technologies have progressed, AI and law has broadened its view from legal-expert systems to legal analytics and, recently, a lot of machine-learning and text-processing techniques have been employed to analyze legal information. The research trends are the same in Japan as well and not only people involved with legal-expert systems, but also those involved with natural language processing as well as lawyers have become interested in AI and law. This report introduces the history of and the research activities on applying AI to the legal domain in Japan.


2021 ◽  
pp. 1-13
Author(s):  
Jenish Dhanani ◽  
Rupa Mehta ◽  
Dipti Rana

Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O (n2). However, there is a limited number of pairs consisting of strong relevance among the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems.


2021 ◽  
Vol 4 (1) ◽  
pp. 88-99
Author(s):  
Mahide Özçelik ◽  
Mukaddes Sakallı Demirok

  The aim of this study is to examine the current research trends in graduate theses on preschool inclusion in Turkey. Document analysis technique, one of the qualitative research methods, was used in the research. It was carried out on 31 graduate theses, which were obtained as a result of scanning the thesis archive of the Higher Education Council (YÖK) and were open to access between 2016 to 2020. The search was carried out by typing "pre-school integration" into the search engine of the Higher Education Institution's thesis archive. The theses included in the study were analyzed using the thesis analysis form prepared beforehand by the researchers. In the prepared form, the theses were analyzed according to the year, thesis level, university, institute, department, thesis supervisor title, research model, number of participants, data collection tool, data analysis method, and domestic and foreign references. The findings obtained within the framework of the examination were interpreted with percentage and frequency tables, and recommendations were made for future research. Key Words: Mainstreaming, preschool, thesis, document analysis.


2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Dina Syafiqa Annur Azhar ◽  
SriRanjani Naidu

This action research attempts to improve teacher’s classroom teaching in developing pupils’ ability to construct simple sentences. Jazz chanting was adapted to teach writing skills and  was used as the intervention. This research aimed to describe the extent the use of jazz chants was able to develop Year 3 pupils’ ability to construct simple sentences effectively. Three research participants  were chosen among year 3 pupils. The participants were purposefully identified as they had encountered difficulties in constructing simple sentences. Three lessons to exploit the intervention were conducted in this research. The data collection methods that were used in this research to gain insights into the effectiveness of the teacher’s teaching strategy were document analysis consisting of worksheets and a pre-test and a post-test, observation and a semi-structured interview with the participants. The research findings revealed that the teaching with the use of the intervention, jazz chanting, was able to positively impact two participants in constructing simple sentences effectively.Meanwhile, triangulation of the various data also indicated that the teaching aided by the intervention failed to have the expected impact on one participant. Discussion of the research findings and conclusion had highlighted the researcher’s pertinent reflection and suggestions for further improvement on the intervention and for future research.


2016 ◽  
Vol 23 (4) ◽  
pp. 1012-1027 ◽  
Author(s):  
Mastura Omar ◽  
Anuar Nawawi ◽  
Ahmad Saiful Azlin Puteh Salin

Purpose The purpose of this paper is to investigate the causes and impact of employee fraud, focusing on one particular industry, namely, the automotive industry. Design/methodology/approach One company was selected as a case for the study. Qualitative data analysis was used for the study, with two techniques for data collection. First was the content or document analysis on various reports, such as employee fraud reports and records of disciplinary action, and second was a series of interviews with employees from different levels and various departments of the company. Findings This study found that the most popular type of fraud is misappropriation of assets, including theft of cash and inventories. No significant differences were seen in terms of fraudster position, as they can come from both the lower and the executive level. However, majority of the fraudsters come from the operational and sales department. This study also found that majority of the fraudsters in the case study were male, new employees and young adults. Their motivations to commit fraud include lack of understanding about fraud behavior, opportunity to commit fraud and lifestyle and financial pressure. Research limitations/implications The results provide further confirmation of the Fraud Triangle Theory and Fraud Diamond Theory on the causes of the fraud. They are also consistent with much prior research and surveys conducted by global professional firms on fraud and its related causes and implications. This study, however, was conducted on only one company with several series of interviews and three years of document analysis. Future research should collect and analyze data from a higher number of companies with more respondents for interviews and longer period for document analysis to get more accurate results. Practical implications This study provides some recommendations for fraud prevention in the future based on real fraud cases and those that involved managing cases up to and including disciplinary decision. These include closed supervision, fraud awareness training, clearer job descriptions, cultivation of a pleasant working environment and improved security control. Social implications This study found that some of the causes of fraud include social factors like lifestyle and financial pressure due to low income. Policy adjustments, such as an effort to push people beyond the poverty line with higher minimum wages, need to be made to prevent low-income workers from seeing their company as another source of illegal income. Originality/value This study is original, as it focuses on a company that operates in the automotive industry, which is rare in fraud literature, particularly in developing markets. In addition, the company is new, so analysis can be conducted on how the company evolved and learned from the fraud analysis for prevention in the future. Furthermore, this study used two techniques of data collection, so that verification of the findings may be made for better reliability.


2012 ◽  
pp. 13-22 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.


Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.


2019 ◽  
Vol 4 (1) ◽  
pp. 405
Author(s):  
Muhammad Rinaldy Bima

This research aims at analyzing the state governance practice which frequently and extraordinarily takes place when governing the state administration, in which the common legal system is unable to accommodate the people's interests. Self-governance is highly necessary that the state function may effectively run independently as the state organ by ensuring respect and compliance of right guaranteed by the state 1945 constitution of the Republic of Indonesia (UUD NRI 1945) as the highest legal document in governing the state. The legal equipment should be able to anticipate various possibilities of emergency conditions to ensure the sustainability of state life


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