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 **Abstract:**
 
-Spatial language is part of everyday life and, therefore, very common in writing. With the exponential increase in the number of textual data on the internet, there has been a lot of demand for the development of machine learning methods to interpret prepositional spatial relations (Radke et al., 2019). In this article, we propose our first trial to build a Multinomial Naive Bayes to tackle the problem. To train and test our model to classify sentences into SPATIAL or NON-SPATIAL, we used a small dataset of examples containing spatial prepositions from grammar websites. The results seem promising although both our corpus and our classifier’s accuracy can still be improved.
+This paper addresses the challenge of interpreting prepositional spatial relations in textual data using machine learning methods. Specifically, we propose using Multinomial Naive Bayes to classify sentences into spatial or non-spatial categories. We trained and tested our model on a small dataset of spatial prepositions extracted from grammar websites. While the results are positive, there is still room for improvement in both the dataset and the classifier's accuracy. The study demonstrates the potential of using Natural Language Processing techniques for spatial language analysis.
 
 **Keywords**: Multinomial Naive Bayes; Spatial Prepositions; Natural Language Processing.