As a result, alternative approaches have been proposed that eschew taxonomies in favor of rating the quality of potential relation arguments (Erk, 2007;Chambers and Jurafsky, 2010) or generating probability distributions over the arguments (Rooth et al., 1999;Pantel et al., 2007;Bergsma et al., 2008;Ritter et al., 2010;Séaghdha, 2010;Bouma, 2010;Jang and Mostow, 2012) in order to obtain higher coverage of preferences.
we overcome the data sparsity of class-based models by leveraging the large quantity of collaboratively-annotated Wikipedia text in order to connect predicate arguments with their semantic class in WordNet using BabelNet (Navigli and Ponzetto, 2012); because we map directly to WordNet synsets, we provide a more readilyinterpretable collocation preference model than most similarity-based or probabilistic models.