For example, Schulte im Walde (2000) uses 153 verbs in 30 classes, and Joanis et al.

However, some of the functions words, prepositions in particular, are known to carry great amount of syntactic information that is related to lexical meanings of verbs (Schulte im Walde, 2003; Brew and Schulte im Walde, 2002; J oanis et al., 2007).

It is therefore unsurprising that much work on verb classification has adopted them as features (Schulte im Walde, 2000; Brew and Schulte im Walde, 2002; Korhonen et al., 2003).

Trying to overcome the problem of data sparsity, Schulte im Walde (2000) explores the additional use of selectional preference features by augmenting each syntactic slot with the concept to which its head noun belongs in an ontology (e.g.

Although the problem of data sparsity is alleviated to certain extent (3), these features do not generally improve classification performance (Schulte im Walde, 2000; J oanis, 2002).

Up to now, such classifications have been used in applications such as word sense disambiguation (Dorr and Jones, 1996; Kohomban and Lee, 2005), machine translation (Prescher et al., 2000; Koehn and Hoang, 2007), document classification (Klavans and Kan, 1998), and in statistical lexical acquisition in general (Rooth et al., 1999; Merlo and Stevenson, 2001; Korhonen, 2002; Schulte im Walde, 2006).

Two large-scale approaches of this kind are Schulte im Walde (2006), who used k-Means on verb subcategorisation frames and verbal arguments to cluster verbs semantically, and J oanis et al.

To the best of our knowledge, Schulte im Walde (2006) is the only hard-clustering approach that previously incorporated selectional preferences as verb features.