Machine Learning
Automatic labeling of semantic roles
Computational Linguistics
Semantic Role Parsing: Adding Semantic Structure to Unstructured Text
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Statistical models for unsupervised prepositional phrase attachment
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
An unsupervised approach to prepositional phrase attachment using contextually similar words
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A nearest-neighbor method for resolving PP-Attachment ambiguity
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
A corpus-based analysis of argument realization by preposition structures
Natural Language Engineering
Prepositions in applications: A survey and introduction to the special issue
Computational Linguistics
Parse correction with specialized models for difficult attachment types
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Prepositional Phrase-attachment is a common source of ambiguity in natural language. The previous approaches use limited information to solve the ambiguity -- four lexical heads -- although humans disambiguate much better when the full sentence is available. We propose to solve the PP-attachment ambiguity with a Support Vector Machines learning model that uses complex syntactic and semantic features as well as unsupervised information obtained from the World Wide Web. The system was tested on several datasets obtaining an accuracy of 93.62% on a Penn Treebank-II dataset; 91.79% on a FrameNet dataset when no manually-annotated semantic information is provided and 92.85% when semantic information is provided.