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

Sleimi, Amin; Ceci, Marcello; Sannier, Nicolas; Sabetzadeh, Mehrdad; Briand, Lionel C.; Dann, John

doi:10.1109/re.2019.00041

Cited by 29 publications

(15 citation statements)

References 36 publications

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“…Another direction is to analyze the usefulness and relevance of these semantic legal metadata in a practical use case, such as a query system to answer the questions that a requirements analyst could ask when elaborating legal requirements. We have already started investigating this direction in (Sleimi et al, 2019), but further work remains, e.g., semantic integration with cross-reference resolution (Sannier et al, 2017), and identifying the true subject of a given legal statement.…”

Section: Discussionmentioning

confidence: 99%

An automated framework for the extraction of semantic legal metadata from legal texts

et al. 2021

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Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97.2% and 82.4%, and recall scores of 94.9% and 92.4%.

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Section: Discussionmentioning

confidence: 99%

An automated framework for the extraction of semantic legal metadata from legal texts

et al. 2021

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“…The refinement of the SQs and the indications of the three expert authors allowed the identification of the following works to ensure that main approaches to formalize specification languages for contracts were included [7,39,53,72,99], legal ontologies [6,48,54], and reviews [27,77]. Concerning the proceedings of recent conferences, the works in [2,5,40,80,82,87,89,90] have been included. The outcome of applying SQs for all six RQs and the refinement process led to the identification of a total of 447 publications.…”

Section: Search and Screening Of Selected Papersmentioning

confidence: 99%

From Legal Contracts to Formal Specifications: A Systematic Literature Review

et al. 2022

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The opportunity to automate and monitor the execution of legal contracts is gaining increasing interest in Business and Academia, thanks to the advent of smart contracts, blockchain technologies, and the Internet of Things. A critical issue in developing smart contract systems is the formalization of legal contracts, which are traditionally expressed in natural language with all the pitfalls that this entails. This paper presents a systematic literature review of papers for the main steps related to the transformation of a legal contract expressed in natural language into a formal specification. Key research studies have been identified, classified, and analyzed according to a four-step transformation process: (a) structural and semantic annotation to identify legal concepts in text, (b) identification of relationships among concepts, (c) contract domain modeling, and (d) generation of a formal specification. Each one of these steps poses serious research challenges that have been the subject of research for decades. The systematic review offers an overview of the most relevant research efforts undertaken to address each step and identifies promising approaches, best practices, and existing gaps in the literature.

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“…Further tasks in legal NLP are also of recent research interest [128], including legal case retrieval, legal case entailment, judicial prediction. Sleimi et al [689] used NLP to query legal requirements and support the legal requirement handling.…”

Section: Nlp Tasksmentioning

confidence: 99%

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Wörmann¹,

Bogdoll²,

Bührle³

et al. 2022

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The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Acknowledgement: The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Climate Action within the project "KI Wissen -Entwicklung von Methoden für die Einbindung von Wissen in maschinelles Lernen". The authors would like to thank the consortium for the successful cooperation.

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

Cited by 29 publications

References 36 publications

An automated framework for the extraction of semantic legal metadata from legal texts

An automated framework for the extraction of semantic legal metadata from legal texts

From Legal Contracts to Formal Specifications: A Systematic Literature Review

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

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