Abstract: Converting natural language into query statements that can be executed in database is the core problem of intelligent interaction and human-computer dialogue system,and is also the urgent need of personalized operation and maintenance system for urban rail trains.At the same time,it is the difficulty of docking the bottom application platform with the support platform for large data application of the new power supply train.The existing neural network-based methods don’t utilizing semantic-rich table content or utilize it partially,which limits the improvement of the execution accuracy.This paper studies how to improve the query accuracy of natural language query interfaces when table content is included in the inputs.Aiming at this problem,this paper proposes a table column embedding method based on table content which embeds the table columns by utilizing the content stored in each table column.Based on the method,this paper proposes a new structure of embedding layer.This paper also proposes a method of data augmentation by utilize table content.It generates new training samples by replacing attribute values in queries with other records in the same column of the table.This paper finally conducts experiments on WikiSQL dataset for the proposed methods of column embedding and data augmentation.The experimental results show that,on the basis of the state-of-the-art methods,the two methods can improve the query accuracy by 0.6%~0.8% when they are used separately and nearly 1% when they are used together.Therefore,it proves that the methods of column embedding and data augmentation proposed in this paper can achieve good improvements on execution accuracy.

Key words: Database query, Natural language processing, SQL, Word embedding