# A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language
The realization of this process mainly relies on the encoding-decoding framework of deep learning models; as explained by [PERSON] [20] in their book _Neural Networks and Deep Learning_, advanced algorithms, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced algorithms, have played a key role in semantic representation and parsing of query statements, which have greatly contributed to the natural language processing technology. Rule-based methods are overly dependent on predefined rules and templates for parsing and converting natural language queries, making it challenging to handle complex and variable semantics. Meanwhile, machine learning methods require manual feature extraction from data to achieve semantic understanding. However, this approach faces challenges, such as time consumption with massive datasets, and the model's effectiveness is significantly impacted by feature quality, thus limiting the extracted features' utility. The emergence of deep learning has, to some extent, alleviated these issues. Deep natural language learning models primarily focus on general natural language structure analysis and semantic expression [21,22], while research on transferring deep learning models to the spatiotemporal query domain remains relatively scarce. Existing pre-trained models are largely based on general corpora and lack a deep understanding of domain-specific terminology and special structures in spatiotemporal queries, resulting in suboptimal performance when processing such specialized terms. More critically, research on spatiotemporal query processing for Chinese language corpora is particularly limited. Given the uniqueness of Chinese text, the complexity of spatiotemporal questions, and the abstractness of entity relations, traditional rule-based and syntactic pattern-based spatiotemporal question parsing methods are difficult to accurately capture the intention of the question and fail to accurately understand the intent of natural language spatiotemporal questions, for example, in processing \"What are the hotels within 3 km north of the Forbidden City in Beijing?\" This kind of complex query statement faces significant challenges. Based on the above background and challenges, this study proposes two core hypotheses:  
(1) With a semantic coding module, by introducing the deep learning model DeBERTa for pre-training feature extraction, feeding the feature vector sequence into BiGRU [23] to learn text features, and combining with the CRF [24] layer to obtain the globally optimal labeling sequences, the entity recognition accuracy and generalization ability in natural language spatiotemporal interrogative sentences can be significantly improved. The model is able to capture the contextual information and semantic features in the text more effectively and thus recognize and classify entities more accurately. (2) Semantic understanding methods complete the classification of interrogative sentences according to the encoding module and the expected query type and realize the conversion of natural language spatiotemporal interrogative sentences to the database language Cypher based on the semantic parsing of the interrogative sentences within the class. In this paper, through the semantic encoding module and the semantic understanding methods, we realize the natural language spatiotemporal question semantics to database conversion model NL2 Cypher. We use Chinese corpus to generate Cypher utterances and conduct an accuracy test; this model has exceeded the performance of other models, and compared with GPT2's, Thousand Questions', and other large models' accuracy has obvious improvement, the conversion accuracy of the training set reaches 88%, and the test set reaches 92%.