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
The nature of statistical learning theory
The nature of statistical learning theory
Automatic labeling of semantic roles
Computational Linguistics
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
Automatic verb classification based on statistical distributions of argument structure
Computational Linguistics
A generative perspective on verb alternations
Computational Linguistics - Special issue on natural language generation
Supertagging: an approach to almost parsing
Computational Linguistics
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
More accurate tests for the statistical significance of result differences
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Generalised PP-attachment disambiguation using corpus-based linguistic diagnostics
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
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
Exploiting strong syntactic heuristics and co-training to learn semantic lexicons
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Learning to distinguish PP arguments from adjuncts
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Exploiting a verb lexicon in automatic semantic role labelling
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A nearest-neighbor method for resolving PP-Attachment ambiguity
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Prepositions in applications: A survey and introduction to the special issue
Computational Linguistics
SemEval-2007 task 06: word-sense disambiguation of prepositions
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Accurate learning for Chinese function tags from minimal features
ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
The role of PP attachment in preposition generation
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
The role of nominalizations in prepositional phrase attachment in GENIA
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Fully unsupervised core-adjunct argument classification
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
TextWiki: a superlative resource
Language Resources and Evaluation
Methodological Review: Approaches to verb subcategorization for biomedicine
Journal of Biomedical Informatics
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In this article we refine the formulation of the problem of prepositional phrase (PP) attachment as a four-way disambiguation problem. We argue that, in interpreting PPs, both knowledge about the site of the attachment (the traditional noun-verb attachment distinction) and the nature of the attachment (the distinction of arguments from adjuncts) are needed. We introduce a method to learn arguments and adjuncts based on a definition of arguments as a vector of features. In a series of supervised classification experiments, first we explore the features that enable us to learn the distinction between arguments and adjuncts. We find that both linguistic diagnostics of argumenthood and lexical semantic classes are useful. Second, we investigate the best method to reach the four-way classification of potentially ambiguous prepositional phrases. We find that whereas it is overall better to solve the problem as a single four-way classification task, verb arguments are sometimes more precisely identified if the classification is done as a two-step process, first choosing the attachment site and then labeling it as argument or adjunct.