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-# Computational-Linguistics[A_Naive_Bayes_Classifier_for_English_Spatial_Prepositions-3.pdf](https://github.com/rmaacario/Computational-Linguistics/files/10437797/A_Naive_Bayes_Classifier_for_English_Spatial_Prepositions-3.pdf)
+*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:
+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.
+
+Keywords: Multinomial Naive Bayes; Spatial Prepositions; Natural Language Processing.