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 # Multinomial Naive Bayes for Classification of English Spatial and Non-spatial Prepositions
 
-Rafael Macário Fernandes
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-Faculty of Philosophy, Languages and Literature, and Human Sciences University of São Paulo, São Paulo, Brazil
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-**Email**: [email protected]
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-**Abstract:**
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-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.
+This project 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 proposed study demonstrates the potential of using Natural Language Processing techniques for spatial language analysis.
 
 **Keywords**: Multinomial Naive Bayes; Spatial Prepositions; Natural Language Processing.