# A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language
## 4 Semantic Understanding Methods  
### Question Classification  
The extraction of semantic information from natural language spatiotemporal questions focuses on the perspective of the question itself. Often, the semantics within a question may be abbreviated or misspelled. After extracting spatiotemporal questions using the semantic encoding module, it is necessary to perform a normalization process on the question set. This involves classifying and semantically parsing the questions to further determine the category to which a spatiotemporal question belongs.  
Question classification aids in better understanding the user's intent and requirements, thereby providing customized query results based on different question categories. This paper uses the benchmark datasets GeoQuestions [36] and GeoQuery [37] for geospatial question answering as a basis, extending categories to include direction, distance, and time question types, such as basic queries, composite queries, and fuzzy queries, and reclassifying the questions. In the geospatial context, the answers expected from spatiotemporal question queries are related to factors such as location, distance, direction, quantity, and extremum. Therefore, as shown in Table 3, questions are classified into eight types based on the expected answer type. These classification methods offer a structured processing framework for spatiotemporal questions, facilitating accurate understanding of user needs and the provision of corresponding query results.  
After semantic encoding, extracted keywords can serve as critical features for distinguishing between question categories. Due to the obvious features of interrogative sentences, this paper adopted Bert (Bidirectional Encoder Representations from Transformers) [38] large model to distinguish different types of interrogative sentences. To accurately distinguish complex queries with similar textual descriptions and question types that return answers in similar formats, a fusion of question vectors and encoded key elements was used, with the final classification results obtained through model training. For example, interrogative keywords were used to differentiate between two complex queries like \"Where is the hotel closest to Tiananmen?\" and \"Which is the hotel closest to Tiananmen?\" Buffer zone queries, extremum queries, and list queries all returned answers as lists of lo  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Question Types** & **Question Examples** \\ \hline Location Query & [Tiananmen Square][nearby][parking lot] where is it? \\ Distance Query & What’s the distance between [Beijing] and [Shanghai]? \\ Direction Query & What direction is [Nanjing] from [Paris]? \\  & What [hotels] are within [3 km] [north] of \\  & [Beijing][Forbidden City]? \\ Count Query & How many [parks] were there in [Beijing] in [2000]? \\  & What are the [closest][eight][hotels] to \\ Maximum/Minimum Query & [Beijing][Forbidden City]? \\ List Query & What [universities] were in [Beijing] in [1980]? \\ Judgment Query & Was [Beijing]’s [population][one million] in the [19 th century]? \\ \hline \hline \end{tabular}
\end{table}
Table 3: Question sentence type.  
cation types, but due to different key elements in the questions, the question type that best reflected the features of the keywords was prioritized in the response.