Learning PP attachment for filtering prosodic phrasing

We explore learning prepositional-phrase attachment in Dutch, to use it as a filter in prosodic phrasing. From a syntactic treebank of spoken Dutch we extract instances of the attachment of prepositional phrases to either a governing verb or noun. Using cross-validated parameter and feature selection, we train two learning algorithms, IB1 and Ripper, on making this distinction, based on unigram and bigram lexical features and a cooccurrence feature derived from WWW counts. We optimize the learning on noun attachment, since in a second stage we use the attachment decision for blocking the incorrect placement of phrase boundaries before prepositional phrases attached to the preceding noun. On noun attachment, IB1 attains an F-score of 83; Ripper an F-score of 79. When used as a filter for prosodic phrasing, using attachment decisions from IB1 yields the best improvement on recall (by four points to 69 F-score) on phrase boundary placement.