Statistical parsing of messages
HLT '90 Proceedings of the workshop on Speech and Natural Language
Foundations of statistical natural language processing
Foundations of statistical natural language processing
From grammar to lexicon: unsupervised learning of lexical syntax
Computational Linguistics - Special issue on using large corpora: II
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Investigating regular sense extensions based on intersective Levin classes
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Automatic acquisition of a large subcategorization dictionary from corpora
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Comlex Syntax: building a computational lexicon
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Automatic extraction of subcategorization frames for Czech
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Statistical filtering and subcategorization frame acquisition
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Using semantically motivated estimates to help subcategorization acquisition
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
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
Assigning verbs to semantic classes via WordNet
SEMANET '02 Proceedings of the 2002 workshop on Building and using semantic networks - Volume 11
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
The Notion of Argument in Prepositional Phrase Attachment
Computational Linguistics
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Towards a semantic classification of Spanish verbs based on subcategorisation information
ACLstudent '04 Proceedings of the ACL 2004 workshop on Student research
A general feature space for automatic verb classification
Natural Language Engineering
A method of creating new valency entries
Machine Translation
A corpus-based analysis of argument realization by preposition structures
Natural Language Engineering
Bengali verb subcategorization frame acquisition: a baseline model
ALR7 Proceedings of the 7th Workshop on Asian Language Resources
Supervised learning of a probabilistic lexicon of verb semantic classes
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
A method of creating new bilingual valency entries using alternations
MLR '04 Proceedings of the Workshop on Multilingual Linguistic Ressources
Unsupervised learning of verb argument structures
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
A computational model of logical metonymy
ACM Transactions on Speech and Language Processing (TSLP) - Special issue on multiword expressions: From theory to practice and use, part 2
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Automatic acquisition of subcategorization lexicons from textual corpora has become increasingly popular. Although this work has met with some success, resulting lexicons indicate a need for greater accuracy. One significant source of error lies in the process of hypothesis selection which is used for removing noise from automatically acquired subcategorization frames (SCFs). In this paper we describe a more accurate semantically-driven approach to hypothesis selection which can be used to improve large-scale SCF acquisition.