Phonology and syntax: the relationship between sound and structure
Phonology and syntax: the relationship between sound and structure
Word association norms, mutual information, and lexicography
Computational Linguistics
Instance-Based Learning Algorithms
Machine Learning
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Information Retrieval
Learning PP attachment from corpus statistics
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Scaling up. Using the WWW to Resolve PP Attachment Ambiguities
KONVENS 2000 / Sprachkommunikation, Vorträge der gemeinsamen Veranstaltung 5. Konferenz zur Verarbeitung natürlicher Sprache (KONVENS), 6. ITG-Fachtagung "Sprachkommunikation"
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
Prepositions in applications: A survey and introduction to the special issue
Computational Linguistics
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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, IB 1 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, IB 1 attains an F-score of 82; RIPPER an F-score of 78. When used as a filter for prosodic phrasing, using attachment decisions from IB 1 yields the best improvement on precision (by six points to 71) on phrase boundary placement.