Computation of sentence similarity score through hybrid deep learning with a special focus on negation sentence

Accurate sentence similarity estimation is a fundamental requirement in Automated Evaluation Systems (AES), where reliable semantic alignment directly impacts grading fairness and consistency. While transformer-based Sentence Similarity Tools (SSTs) perform effectively on non-negated text, they exhibit notable limitations in modeling the semantic distortions introduced by negation. To overcome this challenge, this paper proposes a novel Negation-Aligned Similarity (NAS) Scorer within a hybrid semantic similarity framework, specifically designed for negation-aware semantic modeling. The proposed method integrates multi-embedding fusion using BERT, SBERT, RoBERTa, DistilBERT, and Word2Vec, followed by BiLSTM-based contextual encoding to capture the sequential dependencies. A custom Negation-Sentence-Similarity Dataset (NSSD) comprising 8575 human-verified sentence pairs across four technical domains is curated. Experimental evaluations on the STS Benchmark dataset demonstrate that the proposed NAS Scorer achieves a F1-score of 0.97 after scale normalization, significantly outperforming strong transformer-based baselines. By explicitly addressing negation-induced semantic shifts, the proposed framework enables richer and more reliable similarity estimation, making it well-suited for deployment in real-world AES.