Use BiLSTM with Attention Mechanism to Optimize the Accuracy of Word Meaning Correspondence in Technical Texts

Abstract

In highly specialized and terminology-dense scientific and technical texts, existing word sense disambiguation (WSD) models struggle to adequately model the contextual semantic dependencies of polysemous words, especially in engineering domains where the same term may carry different technical meanings across contexts. To address this issue, this paper proposes a robust WSD model that integrates a bidirectional long shortterm memory network (BiLSTM ) with an attention mechanism, specifically designed for Chinese patent texts. First, a two-layer BiLSTM is used for bidirectional context modeling to capture long-range dependencies. Then, a multi-head attention mechanism uses dynamic weighting to highlight key semantic components, generating highly discriminative context vectors. Finally, a paraphrase alignment mechanism employs bilinear matching to align the context vectors with candidate paraphrase embeddings, thereby reducing semantic confusion. Experiments show that the model achieves a Top-1 accuracy of 88.6 % on high-frequency words, with an average paraphrase alignment similarity of 0.920. In perturbation tests, the average robustness index is 0.159, representing reductions of 62.7%, 53.9%, and 39.5% compared to Word2Vec+CNN, BiLSTM, and BERT, respectively. The method presented in this paper helps to enhance the accuracy and stability of word meaning recognition in technical texts, providing reliable support for knowledge mining and intelligent text processing in technical domains. Its terminology alignment is also useful for engineering corpora where antenna, wavepropagation and materials terms require context-sensitive interpretation.