“…The existing literature on supervised event coreference resolution primarily focuses on designing pairwise classifier based on the surface linguistic features such as lexical features comprising of lemma and part-of-speech tag similarity of event words (Bejan and Harabagiu, 2010;Lee et al, 2012;Liu et al, 2014;Yang et al, 2015;Cremisini and Finlayson, 2020), argument overlap McConky et al, 2012;Sangeetha and Arock, 2012;Bejan and Harabagiu, 2014;Yang et al, 2015;Choubey and Huang, 2017), semantic similarity based on lexical resources such as wordnet (Bejan and Harabagiu, 2010;Liu et al, 2014;Yu et al, 2016) and word embeddings (Yang et al, 2015;Choubey and Huang, 2017;Kenyon-Dean et al, 2018;Barhom et al, 2019;Zuo et al, 2019;Pandian et al, 2020;Sahlani et al, 2020;, and discourse features such as token and sentence distance (Liu et al, 2014;Cybulska and Vossen, 2015). The resulting classifier is used to cluster event mentions.…”