Learning PP attachment for filtering prosodic phrasing

O. van Herwijnen, A. van den Bosch, J.M.B. Terken, E.C. Marsi

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


    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.
    Original languageEnglish
    Title of host publicationProceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics, april 12-17, 2003, Budapest, Hungary
    Place of PublicationNew Brunswick, NJ
    ISBN (Print)1932432000
    Publication statusPublished - 2003


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