  Recent work has considered corpus-based or statistical approaches to the
problem of prepositional phrase attachment ambiguity. Typically, ambiguous verb
phrases of the form {v np1 p np2} are resolved through a model which considers
values of the four head words (v, n1, p and n2). This paper shows that the
problem is analogous to n-gram language models in speech recognition, and that
one of the most common methods for language modeling, the backed-off estimate,
is applicable. Results on Wall Street Journal data of 84.5% accuracy are
obtained using this method. A surprising result is the importance of low-count
events - ignoring events which occur less than 5 times in training data reduces
performance to 81.6%.
