Acquiring predicate-argument mapping information from multilingual texts
Corpus processing for lexical acquisition
Automatic labeling of semantic roles
Computational Linguistics
Class-Based Construction of a Verb Lexicon
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
From grammar to lexicon: unsupervised learning of lexical syntax
Computational Linguistics - Special issue on using large corpora: II
Automatic verb classification based on statistical distributions of argument structure
Computational Linguistics
Using semantic preferences to identify verbal participation in role switching alternations
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Hybrid Natural Language Generation from Lexical Conceptual Structures
Machine Translation
Automatic verb classification using distributions of grammatical features
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Role of word sense disambiguation in lexical acquisition: predicting semantics from syntactic cues
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Clustering verbs semantically according to their alternation behaviour
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Verb class disambiguation using informative priors
Computational Linguistics
Inducing a semantically annotated lexicon via EM-based clustering
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A general feature space for automatic verb classification
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
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
Learning verb argument structure from minimally annotated corpora
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Probabilistic models of verb-argument structure
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A multilingual paradigm for automatic verb classification
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Inducing German semantic verb classes from purely syntactic subcategorisation information
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Semantically motivated subcategorization acquisition
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
Semi-supervised verb class discovery using noisy features
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Extended lexical-semantic classification of English verbs
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
The availability of verb-particle constructions in lexical resources: How much is enough?
Computer Speech and Language
Putting pieces together: combining FrameNet, VerbNet and WordNet for robust semantic parsing
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Mining the web for reciprocal relationships
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
The choice of features for classification of verbs in biomedical texts
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Improving verb clustering with automatically acquired selectional preferences
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
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
One distributional memory, many semantic spaces
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Automatically identifying the source words of lexical blends in english
Computational Linguistics
Verb class discovery from rich syntactic data
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Automatic detection of non-deverbal event nouns for quick lexicon production
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Metaphor identification using verb and noun clustering
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Investigating the cross-linguistic potential of VerbNet: style classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Class-based approach to disambiguating levin verbs
Natural Language Engineering
Distributional memory: A general framework for corpus-based semantics
Computational Linguistics
Modeling reciprocity in social interactions with probabilistic latent space models
Natural Language Engineering
Grouping alternating schemata in semantic valence dictionary of polish verbs
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Hierarchical verb clustering using graph factorization
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
Regular polysemy: a distributional model
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Language Resources and Evaluation
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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Lexical semantic classes of verbs play an important role in structuring complex predicate information in a lexicon, thereby avoiding redundancy and enabling generalizations across semantically similar verbs with respect to their usage. Such classes, however, require many person-years of expert effort to create manually, and methods are needed for automatically assigning verbs to appropriate classes. In this work, we develop and evaluate a feature space to support the automatic assignment of verbs into a well-known lexical semantic classification that is frequently used in natural language processing. The feature space is general – applicable to any class distinctions within the target classification; broad – tapping into a variety of semantic features of the classes; and inexpensive – requiring no more than a POS tagger and chunker. We perform experiments using support vector machines (SVMs) with the proposed feature space, demonstrating a reduction in error rate ranging from 48% to 88% over a chance baseline accuracy, across classification tasks of varying difficulty. In particular, we attain performance comparable to or better than that of feature sets manually selected for the particular tasks. Our results show that the approach is generally applicable, and reduces the need for resource-intensive linguistic analysis for each new classification task. We also perform a wide range of experiments to determine the most informative features in the feature space, finding that simple, easily extractable features suffice for good verb classification performance.