Bilingual Extraction and Alignment of Indigenous Chinese Linguistic Terminology via Multi-Channel Graph Neural Networks

Terms are specialized words and expressions used in particular disciplines, cultures, or fields. They usually carry precise meanings and aim to describe referents accurately and clearly. Due to differences in culture, history, and other factors across countries, the development of indigenous Chinese linguistic terms plays a vital role in bridging cultural gaps and promoting the dissemination of Chinese culture. These terms not only explain specific words in Chinese and describe unique linguistic phenomena, but also embody the core concepts and academic traditions of Chinese linguistics, thereby contributing to the global spread and development of Chinese civilization. In order to achieve cross-linguistic dissemination of indigenous terms, we construct a linguistically informed bilingual corpus encompassing a broad spectrum of linguistic subfields, together with novel methods for the automatic extraction and cross-linguistic alignment of terminologies. The resulting corpus contains over 22,000 aligned sentence pairs across nine linguistic domains, providing a robust foundation for bilingual term mining. Building upon this resource, we further propose a multi-channel graph neural network (MCGNN) that jointly models semantic, syntactic, sequential, and co-occurrence relations, thereby enabling multi-perspective reasoning and achieving more accurate bilingual term extraction and alignment. Experimental results demonstrate that our approach substantially improves the accuracy and consistency of bilingual term extraction, alleviates the resource scarcity in the linguistic domain, and provides a solid foundation for future research and applications in cross-linguistic knowledge sharing and academic communication.