Artificial Intelligence - On connectionist symbol processing
The syntactic process
Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Geometry and Meaning
Pseudo-projective dependency parsing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Introduction to Information Retrieval
Introduction to Information Retrieval
A structured vector space model for word meaning in context
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A regression model of adjective-noun compositionality in distributional semantics
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
Semantic composition with quotient algebras
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Concrete sentence spaces for compositional distributional models of meaning
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Experimental support for a categorical compositional distributional model of meaning
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Domain and function: a dual-space model of semantic relations and compositions
Journal of Artificial Intelligence Research
A comparison of vector-based representations for semantic composition
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
First-order vs. higher-order modification in distributional semantics
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Formal and distributional semantic models offer complementary benefits in modeling meaning. The categorical compositional distributional model of meaning of Coecke et al. (2010) (abbreviated to DisCoCat in the title) combines aspects of both to provide a general framework in which meanings of words, obtained distributionally, are composed using methods from the logical setting to form sentence meaning. Concrete consequences of this general abstract setting and applications to empirical data are under active study (Grefenstette et al., 2011; Grefenstette and Sadrzadeh, 2011). In this paper, we extend this study by examining transitive verbs, represented as matrices in a DisCoCat. We discuss three ways of constructing such matrices, and evaluate each method in a disambiguation task developed by Grefenstette and Sadrzadeh (2011).