An academic English text ambiguity detection algorithm based on contextual word embeddings and deep learning

Abstract

Contextual word embeddings and deep learning architectures have demonstrated remarkable potential in advancing ambiguity detection for academic English texts. This study introduces a lightweight yet highly accurate sequence labeling algorithm that combines domain-adaptive contextual embeddings with a BiLSTM-CRF framework augmented by an attention mechanism. This study addresses the pressing challenge of accurately identifying lexical and syntactic ambiguities in heterogeneous academic corpora. Traditional rule-based and static embedding methods have been unable to meet this requirement. To ensure language diversity and robustness, the corpus from ACL Anthology and IEEE Xplore was comprehensively preprocessed and fine-tuned in a specific field. Experimental evaluation shows that the proposed model achieves state-of-the-art accuracy while maintaining the computational efficiency required for real-time applications. The proposed model significantly outperforms the traditional baselines in terms of both recall and precision. Using model quantization techniques can greatly reduce inference time and detection performance. Contextual representation and deep sequence modeling can achieve reliable and scalable ambiguity detection in challenging academic fields. This suggests that this can provide concrete solutions for machine translation and academic search systems.