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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%.

[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%.

The main hallmark of many forms of familiar and sporadic amyotrophic lateral sclerosis (ALS) is a reduction in nuclear TDP-43 protein and its inclusion in cytoplasmic aggregates in motor neurons. In order to understand which cellular and molecular mechanisms underlie the mislocalization of TDP-43, we examined human skin fibroblasts from two individuals with familial ALS, both with mutations in TDP-43, and two individuals with sporadic ALS, both without TDP-43 mutations or mutations in other ALS related genes. We found that all ALS fibroblasts had a partially cytoplasmic localization of TDP-43 and had reduced cell metabolism as compared to fibroblasts from apparently healthy individuals. ALS fibroblasts showed an increase in global protein synthesis and an increase in 4E-BP1 and rpS6 phosphorylation, which is indicative of mTORC1 activity. We also observed a decrease in glutathione (GSH), which suggests that oxidative stress is elevated in ALS. ERK1/2 activity regulated the extent of oxidative stress and the localization of TDP-43 in the cytoplasm in all ALS fibroblasts. Lastly, ALS fibroblasts showed reduced stress granule formation in response to H2O2 stress. In conclusion, these findings identify specific cellular and molecular defects in ALS fibroblasts, thus providing insight into potential mechanisms that may also occur in degenerating motor neurons.

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.