Generation of Iterative SQL for Classification Based on Traditional Database ORACLE
This paper deeply analyzes the task essence and its sub-task processing mechanism,and solves the problems of column name ambiguity,variable query descriptions,and database data expression differences in Chinese text.NL2SQL research is categorized into pipeline method and deep learning method.Pipeline method converts text to expression and then maps it to SQL,but it is limited by templates and rules,which makes it difficult to deal with language differences flexibly.Deep learning transforms SQL generation into sequence or classification tasks through statistical learning,which captures complex semantics more effectively and improves the flexibility and accuracy of processing natural language queries.In this paper,we innovatively transform SQL generation into a classification task,fine-tune the classifier using a pre-trained model,and successfully migrate and improve the English nl2sql model to the Chinese environment.Experiments demonstrate that this method significantly improves the precision of SQL generation and the accuracy of results.
databaseORACLESQLNL2SQLdeep learning methodnatural language
万方数据
