1. Lexical Acquisition of Verb Direct- Object Selectional Preferences Based on the WordNet Hierarchy Emily Shen and Sushant Prakash

2. Selectional Preferences: V-DO Eat a carrot Drive a truck Eat a truck Drive a carrot Find general classes that a verb takes as arguments Useful for word sense disambiguation, choosing among parses, capturing some essence of semantics, etc.

3. Strategy P(v,c) = (1/N)  n  words(c) (1/|classes(n)|) C(v,n) S(v) = D(P(C|v)||P(C)) =  c P(c|v)log[P(c|v)/P(c)] A(v,c) = P(c|v)log[P(c|v)/P(c)] / S(v) A(v,n) = max c  classes(n) A(v,c) But this assumes flat set of classes – we wanted to exploit the hierarchy: Propagate probability counts to hypernyms. P mod (v,c) = P orig (v,c)+  c_kdes P orig (v,c kdes )

4. This may seem a little screwy… No discount factor for each step up No splitting the count for branches

5. Results Most selective verbs discipline, sigh, slice, shoot down, elongate Least selective verbs make, have, see, get, include Top noun classes plant – plant, explosive device transplant – kidney, internal organ, body part Tested WSD on WSJ and BLLIP. Random baseline: 26.39% P, 100% R, 41.76% F1 Flat WSJ: 28.39% P, 71.67% R, 40.67% F1 Hyper WSJ: 51.44% P, 65.21% R, 57.51% F1

6. Future Work Feed disambiguated nouns into model for training Model class to class relationships Also take into account subject