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

Abstract: 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.

Fariha Khalid, Muhammad Yaseen, Gohar Rahman, Nauman Mazhar, Muhammad Asif Nauman and Aida Mustapha. “Hybrid Feature Learning with TF-IDF and SBERT for Ambiguous Requirement Classification”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.4 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170457

@article{Khalid2026,
title = {Hybrid Feature Learning with TF-IDF and SBERT for Ambiguous Requirement Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170457},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170457},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {4},
author = {Fariha Khalid and Muhammad Yaseen and Gohar Rahman and Nauman Mazhar and Muhammad Asif Nauman and Aida Mustapha}
}