Japanese case frame construction by coupling the verb and its closest case component
HLT '01 Proceedings of the first international conference on Human language technology research
Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences
Computational Linguistics
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
Learning class-to-class selectional preferences
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
A general framework for distributional similarity
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Discriminative learning of selectional preference from unlabeled text
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Abstraction and generalisation in semantic role labels: PropBank, VerbNet or both?
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Latent variable models of concept-attribute attachment
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Semi-supervised semantic role labeling using the latent words language model
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Hypernym discovery based on distributional similarity and hierarchical structures
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
One distributional memory, many semantic spaces
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
A latent dirichlet allocation method for selectional preferences
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Latent variable models of selectional preference
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
An experiment in detection and correction of malapropisms through the web
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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Learning Selectional Preferences has been approached as a verb and argument problem, or at most as a tri-nary relationship between subject, verb and object. The correlation of all arguments in a sentence, however, has not been extensively studied for sentence plausibility measuring because of the increased number of potential combinations and data sparseness. We propose a unified model for machine learning using SVM (Support Vector Machines) with features based on topic-projected words from a PLSI (Probabilistic Latent Semantic Indexing) Model and PMI (Pointwise Mutual Information) as cooccurrence features, and WordNet top concept projected words as semantic classes. We perform tests using a pseudo-disambiguation task. We found that considering all arguments in a sentence improves the correct identification of plausible sentences with an increase of 10% in recall among other things.