Measuring Orthogonal Mechanics in Linguistic Annotation Games

Abstract

Gamification has been recently growing in popularity among researchers investigating Information and Communication Technologies. Scholars have been trying to take advantage of this approach in the field of natural language processing (NLP), developing Games With A Purpose (GWAPs) for corpus annotation that have obtained encouraging results both in annotation quality and overall cost. However, GWAPs implement gamification in different ways and to different degrees. We propose a new framework to investigate the mechanics employed in the gamification process and their magnitude in terms of complexity. This framework is based on an analysis of some of the most important contributions in the field of NLP-related gamified applications and GWAP theory. Its primary purpose is to provide a first step towards classifying mechanics that mimic mainstream video games and may require skills that are not relevant to the annotation task, defined as orthogonal mechanics. In order to test our framework, we develop and evaluate Spacewords, a linguistic space game for synonymy annotation.