Cross-Domain Ambiguity Detection using Linear Transformation of Word Embedding Spaces
This addresses the problem of misinterpretation in software requirements elicitation for stakeholders from diverse backgrounds, though it is an incremental improvement over existing methods.
The paper tackles cross-domain ambiguity in requirements engineering by proposing a method that uses linear transformations on word embeddings to detect potentially ambiguous words across different domains, helping to prevent misunderstandings among stakeholders.
The requirements engineering process is a crucial stage of the software development life cycle. It involves various stakeholders from different professional backgrounds, particularly in the requirements elicitation phase. Each stakeholder carries distinct domain knowledge, causing them to differently interpret certain words, leading to cross-domain ambiguity. This can result in misunderstanding amongst them and jeopardize the entire project. This paper proposes a natural language processing approach to find potentially ambiguous words for a given set of domains. The idea is to apply linear transformations on word embedding models trained on different domain corpora, to bring them into a unified embedding space. The approach then finds words with divergent embeddings as they signify a variation in the meaning across the domains. It can help a requirements analyst in preventing misunderstandings during elicitation interviews and meetings by defining a set of potentially ambiguous terms in advance. The paper also discusses certain problems with the existing approaches and discusses how the proposed approach resolves them.