COMPUTATIONAL APPROACHES AND LINGUISTIC ANNOTATION STRATEGIES FOR CONSTRUCTING UZBEK–CHINESE PARALLEL CORPUS RESOURCES: DATA COLLECTION, SENTENCE ALIGNMENT, AND QUALITY EVALUATION

Uzbek–Chinese is a low-resource language pair for which large, curated parallel corpora remain scarce, despite growing academic, economic and cultural exchange between Uzbekistan and China. This article examines computational approaches and linguistic annotation strategies for constructing Uzbek–Chinese parallel corpus resources. It reviews data collection channels, preprocessing problems specific to the two languages, embedding-based sentence alignment, filtering methods and annotation layers, and it proposes a quality evaluation framework that combines manual auditing with automatic and downstream measures. The analysis draws on recent work on multilingual sentence embeddings, linear-time sentence alignment, audits of web-crawled multilingual data and multilingual evaluation benchmarks. The article argues that the quality of an Uzbek–Chinese corpus depends less on its size than on script normalization, segmentation consistency, alignment confidence and documented human evaluation. A practical pipeline is outlined, together with annotation guidelines and indicators that allow corpus quality to be reported transparently.