The meaning of natural language expressions varies, sometimes dramatically, along a dimensions including subjective bias (e.g., what’s funny / offensive for one person may not be for another; Akhtar et al, 2021; Almanea & Poesio, 2022; Kocon et al, 2021; Leonardelli et al, 2021), ambiguity (e.g., the question of what a pronoun like “he” refers to in a given context, Poesio & Artstein, 2005; Versley, 2008; Recasens et al, 2011; Passonneau et al, 2012; Plank et al, 2014; Pavlick & Kwiatkowski, 2019), and vagueness (e.g., what data dimensions and thresholds do we apply when we call the weather “mild”, or the condition of a patient “stable”? Van Deemter 2010, Douven et al. 2013), among others. Such variation in meaning raises serious challenges for NLP. The objective of this project is to carry out fundamental as well as applicable research on meaning variation in NLP along several dimensions of variation, exploring the interconnections between them and the implications for NLP research and applications. The project is funded by NWO, through a AINed Fellowship grant to Massimo Poesio, the University of Utrecht's NLP group  ( AI and Data Science division) and the Institute for Language Sciences.