Abstract:

Abstract An approach for pragmatic ambiguity detection in natural language requirements is presented in this paper. Pragmatic ambiguities are determined by the requirements' context, which includes the reader's background knowledge. Readers with different backgrounds may interpret requirements differently. To determine whether a requirement is ambiguous or not, various pragmatic interpretations are compared. In this paper, we will discuss the significance of pragmatic ambiguity detection in NLRs, applications of NLP, ambiguities in NLP, and pragmatic ambiguities, as well as review various techniques used for identifying and resolving ambiguities in natural language requirements. Our objective is to motivate further research in this field by providing a thorough understanding of the difficulties and opportunities related to pragmatic ambiguity detection in NLRs. The tool might be enhanced in the future to support more file types, like PDF. There is ongoing research in the area of pragmatic ambiguity detection, and new approaches and procedures are constantly being developed. It is likely that improvements in pragmatic ambiguity detection and resolution will come as a result of developments in artificial intelligence, machine learning, and natural language processing in the future. Additionally, the growing accessibility of expansive, varied datasets will make it possible to train more reliable and accurate models. Pragmatic ambiguity detection is likely to become a more crucial tool as the field develops in fields like automated language translation, dialogue systems, and natural language understanding.

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