@article{uninimx11174,
            year = {2024},
         journal = {IEEE Access},
          author = {Khadija Aslam and Faiza Iqbal and Ayesha Altaf and Naveed Hussain and M{\'o}nica Gracia Villar and Emmanuel Soriano Flores and Isabel De La Torre Diez and Imran Ashraf},
           month = {Febrero},
           title = {Detecting Pragmatic Ambiguity in Requirement Specification Using Novel Concept Maximum Matching Approach Based on Graph Network},
           pages = {1--1},
        abstract = {Requirements specifications written in natural language enable us to understand a program?s intended functionality, which we can then translate into operational software. At varying stages of requirement specification, multiple ambiguities emerge. Ambiguities may appear at several levels including the syntactic, semantic, domain, lexical, and pragmatic levels. The primary objective of this study is to identify requirements? pragmatic ambiguity. Pragmatic ambiguity occurs when the same set of circumstances can be interpreted in multiple ways. It requires consideration of the context statement of the requirements. Prior research has developed methods for obtaining concepts based on individual nodes, so there is room for improvement in the requirements interpretation procedure. This research aims to develop a more effective model for identifying pragmatic ambiguity in requirement definition. To better interpret requirements, we introduced the Concept Maximum Matching (CMM) technique, which extracts concepts based on edges. The CMM technique significantly improves precision because it permits a more accurate interpretation of requirements based on the relative weight of their edges. Obtaining an F-measure score of 0.754 as opposed to 0.563 in existing models, the evaluation results demonstrate that CMM is a substantial improvement over the previous method.},
             url = {http://repositorio.unini.edu.mx/id/eprint/11174/},
        keywords = {Pragmatic ambiguity, natural language, requirements specification, knowledge base, ambiguity detection}
}