This paper discusses the interpretation of nominalizations in domain independent wide-coverage text. We present a statistical model which interprets nominalizations based on the cooccurrence of verb-argument tuples in a large balanced corpus. We propose an algorithm which treats the interpretation task as a disambiguation problem and achieves a performance of approximately 80 % by combining partial parsing, smoothing techniques and domain independent taxonomic information (e.g., WordNet)
