Information-based syntax and semantics: Vol. 1: fundamentals
Information-based syntax and semantics: Vol. 1: fundamentals
C4.5: programs for machine learning
C4.5: programs for machine learning
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
Supertagging: an approach to almost parsing
Computational Linguistics
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
Automatic distinction of arguments and modifiers: the case of prepositional phrases
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Learning argument/adjunct distinction for Basque
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
Semantically motivated subcategorization acquisition
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
Learning to distinguish PP arguments from adjuncts
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
The Notion of Argument in Prepositional Phrase Attachment
Computational Linguistics
Prepositions in applications: A survey and introduction to the special issue
Computational Linguistics
Lexical and structural biases for function parsing
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Using a logic programming framework to control database query dialogues in natural language
ICLP'06 Proceedings of the 22nd international conference on Logic Programming
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We propose a new formulation of the PP attachment problem as a 4-way classification which takes into account the argument or adjunct status of the PP. Based on linguistic diagnostics, we train a 4-way classifier that reaches an average accuracy of 73.9% (baseline 66.2%). Compared to a sequence of binary classifiers, the 4-way classifier reaches better performance and individuates a verb's arguments more accurately, thus improving the acquisition of a crucial piece of information for many NLP applications.