PP Attachment Ambiguity Resolution through Supervised Learning

Abstract

This paper deals with two important ambiguities of natural language: prepositional phrase attachment and word sense ambiguity. We propose a new supervised learning method for PP-attachment based on a semantically tagged corpus. Because any sufficiently big sense-tagged corpus does not exist, we also propose a new unsupervised context based word sense disambiguation algorithm which amends the training corpus for the PP attachment by word sense tags. We present the results of our approach, which not only surpasses any existing method but also draws near human performance.