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
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Automatic verb classification based on statistical distributions of argument structure
Computational Linguistics
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
Applied morphological processing of English
Natural Language Engineering
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Investigating regular sense extensions based on intersective Levin classes
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Clustering verbs semantically according to their alternation behaviour
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Verb class disambiguation using informative priors
Computational Linguistics
Acquiring lexical generalizations from corpora: a case study for diathesis alternations
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Inducing history representations for broad coverage statistical parsing
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Improving subcategorization acquisition using word sense disambiguation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Clustering polysemic subcategorization frame distributions semantically
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Constructing semantic space models from parsed corpora
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A general feature space for automatic verb classification
Natural Language Engineering
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
Classifier optimization and combination in the English all words task
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
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Lapata and Brew (Computational Linguistics, vol. 30, 2004, pp. 295–313) (hereafter LB04) obtain from untagged texts a statistical prior model that is able to generate class preferences for ambiguous Lewin (English Verb Classes and Alternations: A Preliminary Investigation, 1993, University of Chicago Press) verbs (hereafter Levin). They also show that their informative priors, incorporated into a Naive Bayes classifier deduced from hand-tagged data (HTD), can aid in verb class disambiguation. We re-analyse LB04's prior model and show that a single factor (the joint probability of class and frame) determines the predominant class for a particular verb in a particular frame. This means that the prior model cannot be sensitive to fine-grained lexical distinctions between different individual verbs falling in the same class. We replicate LB04's supervised disambiguation experiments on large-scale data, using deep parsers rather than the shallow parser of LB04. In addition, we introduce a method for training our classifier without using HTD. This relies on knowledge of Levin class memberships to move information from unambiguous to ambiguous instances of each class. We regard this system as unsupervised because it does not rely on human annotation of individual verb instances. Although our unsupervised verb class disambiguator does not match the performance of the ones that make use of HTD, it consistently outperforms the random baseline model. Our experiments also demonstrate that the informative priors derived from untagged texts help improve the performance of the classifier trained on untagged data.