Hybrid Feature Learning with TF-IDF and SBERT for Ambiguous Requirement Classification

Ambiguity in Software Requirement Specifications (SRS) remains a major source of project delay, rework, and misinterpretation in software engineering. Traditional ambiguity detection approaches rely on lexical or rule-based techniques that capture surface-level patterns but fail to model contextual meaning. Recent transformer-based models improve semantic representation; however, when applied independently, they often overlook lexical ambiguity and remain sensitive to class imbalance. This study proposes a hybrid feature learning framework that integrates TF-IDF lexical representations with Sentence-BERT (SBERT) contextual embeddings for ambiguous requirement classification. The approach is evaluated on the Functional–Non-Functional Requirements (FR–NFR) dataset using Logistic Regression, Random Forest, and Support Vector Machine classifiers. Experimental results demonstrate that single-feature models produce unstable precision–recall trade-offs, particularly under severe class imbalance. In contrast, the proposed TF-IDF + SBERT hybrid representation consistently improves recall and F1-score. The best performance is achieved using Support Vector Machine, attaining an F1-score of 0.7122 and a recall of 0.6429, significantly outperforming standalone lexical and semantic baselines. The findings confirm that ambiguity detection is a multi-dimensional problem requiring both lexical frequency patterns and contextual semantic modelling. The proposed framework offers a reproducible and practically deployable solution for automated ambiguity detection in software requirements engineering.