From 943f3cdd71e4058389caad7783ae76c9e115aa38 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Rafael=20Mac=C3=A1rio=20Fernandes?=
 <86082684+rmaacario@users.noreply.github.com>
Date: Sun, 2 Apr 2023 13:43:33 -0300
Subject: [PATCH] Update README.md

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-*Multinomial Naive Bayes for Classification of English Spatial and Non-spatial Prepositions*
+# Multinomial Naive Bayes for Classification of English Spatial and Non-spatial Prepositions
 
 Rafael Macário Fernandes
 
 Faculty of Philosophy, Languages and Literature, and Human Sciences University of São Paulo, São Paulo, Brazil
 
-Email: rafael.macario@usp.br
+**Email**: rafael.macario@usp.br
+
+**Abstract:**
 
-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.
+**Keywords**: Multinomial Naive Bayes; Spatial Prepositions; Natural Language Processing.