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Text preprocessing is often the first step in the pipeline of a Natural Language Processing (NLP) system, with potential impact in its final performance. Despite its importance, text preprocessing has not received much attention in the deep learning literature. In this paper we investigate the impact of simple text preprocessing decisions (particularly tokenizing, lemmatizing, lowercasing and multiword grouping) on the performance of a standard neural text classifier. We perform an extensivedoi:10.18653/v1/w18-5406 dblp:conf/emnlp/Camacho-Collados18 fatcat:tzh57m2hdzb25d6fyzsu6kq7vi