An Approach for Detecting Modality and Negation in Texts by Using Rule-based Techniques

The automatically processing of texts has become a very important aim in the recent years because of the huge amount of time that could be saved in sectors such as education, jurisprudence or medicine. Thus, a very important task in the automatically processing of texts is the detection of language mechanism, such as modality and negation. In this paper we present our first approach in the field of detection of modality and negation by using a rulebased system. The system uses lexical and syntactic information found in the text to determine where exist evidentially of modality or negation. The best results obtained by our approach in detecting modality and negation achieves a macroavaraged F1 measurement of 0.5339, a Microavaraged F1 of 0.6395, and an overall accuracy of 0.6551.