Proceedings of the 1992 ACM/IEEE conference on Supercomputing
A probabilistic account of logical metonymy
Computational Linguistics
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Using a probabilistic class-based lexicon for lexical ambiguity resolution
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Early lexical development in a self-organizing neural network
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Journal of Cognitive Neuroscience
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
What is word meaning, really?: (and how can distributional models help us describe it?)
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
Predicting the semantic compositionality of prefix verbs
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Very high accuracy and fast dependency parsing is not a contradiction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Distributional memory: A general framework for corpus-based semantics
Computational Linguistics
Computing semantic compositionality in distributional semantics
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Composing and updating verb argument expectations: a distributional semantic model
CMCL '11 Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics
| Hi-index | 0.00 |
Logical metonymies (The student finished the beer) represent a challenge to compositionality since they involve semantic content not overtly realized in the sentence (covert events → drinking the beer). We present a contrastive study of two classes of computational models for logical metonymy in German, namely a probabilistic and a distributional, similarity-based model. These are built using the SDeWaC corpus and evaluated against a dataset from a self-paced reading and a probe recognition study for their sensitivity to thematic fit effects via their accuracy in predicting the correct covert event in a metonymical context. The similarity-based models allow for better coverage while maintaining the accuracy of the probabilistic models.