This paper describes a cross-discip

This paper describes a cross-disciplinary extension of previous work on inferring the meanings of unknown verbs from
context. In earlier work, a computational model was developed to incrementally infer meanings while processing texts in
an information extraction task setting. In order to explore the
space of possible predictors that the system could use to infer
verb meanings, we performed a statistical analysis of the corpus that had been used to test the computational system. There
were various syntactic and semantic features of the verbs that
were significantly diagnostic in determining verb meaning. We
also evaluated human performance at inferring the verb in the
same set of sentences. The overall number of correct predictions for humans was quite similar to that of the computational
system, but humans had higher precision scores. The paper
concludes with a discussion of the implications of these statistical and experimental findings for future computational work