# A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language  
[PERSON]  
1 School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China; [EMAIL_ADDRESS]. of complex Geographic Information Systems (GIS) or structured query languages is undoubtedly an innovative and practical attempt [4]. Spatiotemporal queries refer to query statements involving temporal and spatial attributes, typically combining geographic and temporal information for understanding and answering. Semantic understanding is a key technology in spatiotemporal query processing [5]. Converting natural language spatiotemporal queries into structured query statements is currently a hot research topic in natural language processing. Three main approaches address this challenge: rule-based methods, machine learning, and deep learning. Rule-based methods utilize longest matching word segmentation with spatial knowledge base support to perform word segmentation on query statements while using spatial query sentence pattern templates for syntactic analysis [6], thereby interpreting spatial query targets and corresponding spatial operations [7]. Natural language questions are converted into standard spatial query statements and executed by relational databases, with results presented in textual or graphical form [8]. Reference [9] proposed a rule-matching model based on edit distance for matching words and sentence patterns in queries. Reference [10] combined rule-based and statistical models to extract spatiotemporal and attribute information from Chinese texts. In terms of query processing, however, methods based on statistical models are not suitable for performing the necessary data extraction tasks [11]. Reference [12] conducted a statistical analysis and classification of predicates and quantifiers describing spatial relationships in natural language, establishing four syntactic patterns for natural language spatial queries. Regarding structured query language conversion, the common approach is to implement natural language to structured language mapping through template construction [13; 14]. Reference [15] designed a template-based question answering system that converts natural language questions in the geospatial dimension into GeoSQL (Geo Spatial SQL) queries. Machine learning methods construct models using corpus data and machine learning algorithms, automatically converting natural language queries into query statements by learning the correspondence between language and databases. References [16; 17] employ pre-trained named entity recognition models and dictionary lookup methods to identify geographic entities and utilize constituent syntax to extract spatial relationships between different entities. Through semantic constraint syntax, they extract spatial relationships between entities, and after annotating geographic entities and spatial relationship terms, they map their semantics to predefined templates [18]. In recent years, with the rapid accumulation of big data and the significant improvement of computer performance, deep learning methods have become a hotspot for many scholars' research. [PERSON] et al. [19] have deeply analyzed the core principle of deep learning technology and laid a solid theoretical foundation for exploring its application in the field of natural language processing. In the field of natural language processing, the application of deep learning models has become more and more widespread, especially in the task of transforming natural language problems into query statements, where it shows a powerful ability.