Investigating Advanced Translation Techniques for Traditional Chinese Medicine Texts through Knowledge Mapping

Abstract

To refine the translation of long English sentences in the context of Traditional Chinese Medicine (TCM), this study constructs a top-level model and framework for the TCM domain, incorporating a knowledge graph. It introduces the ALBERT-BiLSTM-CRF model for entity recognition within TCM texts, enhancing processing and translation accuracy. By leveraging knowledge mapping and analyzing the unique features of TCM-related English sentences, the paper proposes an optimized translation methodology. Control experiments demonstrate the ALBERT-BiLSTM[1]CRF model's superiority in recognition efficiency and the proposed translation methods' high accuracy, proving their efficacy for translating complex TCM texts.