The predominant unsupervised approach for learning inference rules between templates is via distributional similarity (Lin and Pantel, 2001; Ravichandran and Hovy, 2002;Szpektor and Dagan, 2008).
each argument slot in a template is represented by an argument vector, containing the words (or terms) that instantiate this slot in all of the occurrences of the template in a learning corpus.