Answering Complex Queries on Legal Networks: A Direct and a Structured IR Approaches

Mimouni, Nada; Nazarenko, Adeline; Salotti, Sylvie

doi:10.1007/978-3-030-00178-0_31

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…A marginal approach considers documents as vertices and relies on the connectivity between documents (Kelleher and Luz 2005). The goal is to capture the intertextuality expressed through the hyperlinks of web documents, in legal document networks for Mimouni, Nazarenko, and Salotti (2015) or in chats for Abilhoa and de Castro (2014). The underlying idea is that documents related to a document d within a corpus provide additional information for identifying relevant keywords for d .…”

Section: Extraction Methodsmentioning

confidence: 99%

Keyword extraction: Issues and methods

Firoozeh

Nazarenko

Alizon³

et al. 2019

Nat. Lang. Eng.

Self Cite

114

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Due to the considerable growth of the volume of text documents on the Internet and in digital libraries, manual analysis of these documents is no longer feasible. Having efficient approaches to keyword extraction in order to retrieve the ‘key’ elements of the studied documents is now a necessity. Keyword extraction has been an active research field for many years, covering various applications in Text Mining, Information Retrieval, and Natural Language Processing, and meeting different requirements. However, it is not a unified domain of research. In spite of the existence of many approaches in the field, there is no single approach that effectively extracts keywords from different data sources. This shows the importance of having a comprehensive review, which discusses the complexity of the task and categorizes the main approaches of the field based on the features and methods of extraction that they use. This paper presents a general introduction to the field of keyword/keyphrase extraction. Unlike the existing surveys, different aspects of the problem along with the main challenges in the field are discussed. This mainly includes the unclear definition of ‘keyness’, complexities of targeting proper features for capturing desired keyness properties and selecting efficient extraction methods, and also the evaluation issues. By classifying a broad range of state-of-the-art approaches and analysing the benefits and drawbacks of different features and methods, we provide a clearer picture of them. This review is intended to help readers find their way around all the works related to keyword extraction and guide them in choosing or designing a method that is appropriate for the application they are targeting.

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Section: Extraction Methodsmentioning

confidence: 99%

Keyword extraction: Issues and methods

Firoozeh

Nazarenko

Alizon³

et al. 2019

Nat. Lang. Eng.

Self Cite

114

View full text Add to dashboard Cite

show abstract

“…Perhaps the most illustrative of these limitations is the inability of the systems to understand the question instead of For this reason, the existing approaches are focused on developing good ideas that allow to answer the questions asked and obviate the performance details since these can be solved with these new forms of high-performance computing. Some outstanding works in this direction are proposed by Bennet et al with a focus on the scalability of the solution by design (Bennett et al, 2017), or Mimouni et al for handling the problem of complex queries by working with approximate answers and richer semantics (Mimouni et al, 2017). Last but not least, there is also a novel approach based on the concept of mutual information exchange (Martinez-Gil et al, 2019a), which, when applied to large volumes of data, have demonstrated better performance than classic co-occurrence methods.…”

Section: Big Data Solutionsmentioning

confidence: 99%

A Survey on Legal Question Answering Systems

Martinez‐Gil¹

2021

Preprint

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Many legal professionals think that the explosion of information about local, regional, national, and international legislation makes their practice more costly, time-consuming, and even error-prone. The two main reasons for this are that most legislation is usually unstructured, and the tremendous amount and pace with which laws are released causes information overload in their daily tasks. In the case of the legal domain, the research community agrees that a system allowing to generate automatic responses to legal questions could substantially impact many practical implications in daily activities.The degree of usefulness is such that even a semi-automatic solution could significantly help to reduce the workload to be faced. This is mainly because a Question Answering system could be able to automatically process a massive amount of legal resources to answer a question or doubt in seconds, which means that it could save resources in the form of effort, money, and time to many professionals in the legal sector. In this work, we quantitatively and qualitatively survey the solutions that currently exist to meet this challenge.

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“…Within the first type of systems, the Légilocal system [26] and the Nomothesia platform [27] propose solutions for authorities to manage local regulations implementing national laws in France and Greece, respectively. Their conceptual models cover legal document types as well as structural, geographical and topographical metadata, but do not provide semantic metadata about the content of the provisions.…”

Section: B Legal Search and Analysis In Ai And Lawmentioning

confidence: 99%

A Query System for Extracting Requirements-Related Information from Legal Texts

Sleimi

Ceci

Sannier

et al. 2019

2019 IEEE 27th International Requirements Engineering Conference (RE)

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Searching legal texts for relevant information is a complex and expensive activity. The search solutions offered by present-day legal portals are targeted primarily at legal professionals. These solutions are not adequate for requirements analysts whose objective is to extract domain knowledge including stakeholders, rights and duties, and business processes that are relevant to legal requirements. Semantic Web technologies now enable smart search capabilities and can be exploited to help requirements analysts in elaborating legal requirements. In our previous work, we developed an automated framework for extracting semantic metadata from legal texts. In this paper, we investigate the use of our metadata extraction framework as an enabler for smart legal search with a focus on requirements engineering activities. We report on our industrial experience helping the Government of Luxembourg provide an advanced search facility over Luxembourg's Income Tax Law. The experience shows that semantic legal metadata can be successfully exploited for answering requirements engineering-related legal queries. Our results also suggest that our conceptualization of semantic legal metadata can be further improved with new information elements and relations.

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Answering Complex Queries on Legal Networks: A Direct and a Structured IR Approaches

Cited by 4 publications

References 7 publications

Keyword extraction: Issues and methods

Keyword extraction: Issues and methods

A Survey on Legal Question Answering Systems

A Query System for Extracting Requirements-Related Information from Legal Texts

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