New types of computational perceptions: Linguistic descriptions in deforestation analysis. (1st November 2017) - Conde-Clemente, Patricia; Alonso, Jose M.; Nunes, Éldman O.; Sanchez, Angel; Trivino, Gracian
Highlights: We extend the concept of Computational Perception to handle reliability of data. We introduce three new types of Computational Perceptions to enrich generated texts. We generate automatic reports about deforestation evolution in the Amazon region. We test empirically the quality of the reports in a web survey with 30 subjects. We evaluate linguistic descriptions through fuzzy rating scale-based questionnaires. Abstract: Automatic linguistic description of the available data about complex phenomena is a challenging task that is receiving the attention of data scientists in recent years. As an evolution of previous research results, there is a need of creating new linguistic computational models that allow us dealing with more complex phenomena and more complex descriptions of a growing amount of heterogeneous and real-time data. This paper contributes to this field by presenting three new ways of describing added-value information automatically extracted from data. Also, we extend previous computational models by including a description of the reliability of the available input data. Namely, we face this challenge by using a new implementation of the concept of Z-number proposed by Zadeh. We demonstrate the possibilities of the proposed extension with a practical application. The application generates automatic linguistic reports about the deforestation evolution in the Amazon region, e.g., "The deforestation last month was high. Because of the cloudiness, the reliability of this information is moderate". Additionally, we evaluate the quality of the generated linguistic descriptions through fuzzy rating scale-based questionnaires. Moreover, we have also made a comparative study between reports generated with and without the new contributions introduced in this paper. The results show that the new types of computational perceptions introduced in this paper are ready to help data scientists to automatically generate good quality reports.