An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts

in Empirical Software Engineering (2021), 26(3), 43

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 ... [more ▼]

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%. [less ▲]

Automated Recommendation of Templates for Legal Requirements

in Proceedings of the 28th IEEE International Requirements Engineering Conference (RE'20) (2020)

[Context] In legal requirements elicitation, requirements analysts need to extract obligations from legal texts. However, legal texts often express obligations only indirectly, for example, by attributing ... [more ▼]

[Context] In legal requirements elicitation, requirements analysts need to extract obligations from legal texts. However, legal texts often express obligations only indirectly, for example, by attributing a right to the counterpart. This phenomenon has already been described in the Requirements Engineering (RE) literature. [Objectives] We investigate the use of requirements templates for the systematic elicitation of legal requirements. Our work is motivated by two observations: (1) The existing literature does not provide a harmonized view on the requirements templates that are useful for legal RE; (2) Despite the promising recent advancements in natural language processing (NLP), automated support for legal RE through the suggestion of requirements templates has not been achieved yet. Our objective is to take steps toward addressing these limitations. [Methods] We review and reconcile the legal requirement templates proposed in RE. Subsequently, we conduct a qualitative study to define NLP rules for template recommendation. [Results and Conclusions] Our contributions consist of (a) a harmonized list of requirements templates pertinent to legal RE, and (b) rules for the automatic recommendation of such templates. We evaluate our rules through a case study on 400 statements from two legal domains. The results indicate a recall and precision of 82,3% and 79,8%, respectively. We show that introducing some limited interaction with the analyst considerably improves accuracy. Specifically, our human-feedback strategy increases recall by 12% and precision by 10,8%, thus yielding an overall recall of 94,3% and overall precision of 90,6%. [less ▲]

Automated Extraction of Semantic Legal Metadata Using Natural Language Processing

in the 26th IEEE International Requirements Engineering Conference, Banff, Alberta, 20-24 August 2018 (2018, August)

[Context] Semantic legal metadata provides information that helps with understanding and interpreting the meaning of legal provisions. Such metadata is important for the systematic analysis of legal ... [more ▼]

[Context] Semantic legal metadata provides information that helps with understanding and interpreting the meaning of legal provisions. Such metadata is important for the systematic analysis of legal requirements. [Objectives] 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 further does not exploit the full potential of natural language processing (NLP). Our objective is to take steps toward addressing these limitations. [Methods] We review and reconcile the semantic legal metadata types proposed in RE. Subsequently, we conduct a qualitative study aimed at investigating how the identified metadata types can be extracted automatically. [Results and Conclusions] We propose (1) a harmonized conceptual model for the semantic metadata types pertinent to legal requirements analysis, and (2) automated extraction rules for these metadata types based on NLP. We evaluate the extraction rules through a case study. Our results indicate that the rules generate metadata annotations with high accuracy. [less ▲]