WordNet: a lexical database for English
Communications of the ACM
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Class-based probability estimation using a semantic hierarchy
Computational Linguistics
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Experiments on the choice of features for learning verb classes
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
From trees to predicate-argument structures
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Improving subcategorization acquisition using word sense disambiguation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
Clustering Syntactic Positions with Similar Semantic Requirements
Computational Linguistics
Experiments on the Automatic Induction of German Semantic Verb Classes
Computational Linguistics
Automatic classification of verbs in biomedical texts
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Automated induction of sense in context
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
The second release of the RASP system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
Inducing classes of terms from text
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Verb class discovery from rich syntactic data
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Deriving generalized knowledge from corpora using WordNet abstraction
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Building a semantic lexicon of English nouns via bootstrapping
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
A social dimensional cyber threat model with formal concept analysis and fact-proposition inference
International Journal of Information and Computer Security
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Assigning arguments of verbs to different semantic classes ('semantic typing'), or alternatively, checking the 'selectional restrictions' of predicates, is a fundamental component of many natural language processing tasks. However, a common experience has been that general purpose semantic classes, such as those encoded in resources like WordNet, or handcrafted subject-specific ontologies, are seldom quite right when it comes to analysing texts from a particular domain. In this paper we describe a method of automatically deriving fine-grained, domain-specific semantic classes of arguments while simultaneously clustering verbs into semantically meaningful groups: the first step in verb sense induction. We show that in a small pilot study on new examples from the same domain we are able to achieve almost perfect recall and reasonably high precision in the semantic typing of verb arguments in these texts.