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
Toward discourse representation via pregroup grammars
Journal of Logic, Language and Information
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
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
Semantic Vector Models and Functional Models for Pregroup Grammars
Journal of Logic, Language and Information
QI'11 Proceedings of the 5th international conference on Quantum interaction
Experimenting with transitive verbs in a DisCoCat
GEMS '11 Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics
Distributional models and lexical semantics in convolution kernels
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Entailment above the word level in distributional semantics
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Evaluating distributional models of semantics for syntactically invariant inference
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Learning semantics and selectional preference of adjective-noun pairs
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
Saarland: vector-based models of semantic textual similarity
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
Distributional techniques for philosophical enquiry
LaTeCH '12 Proceedings of the 6th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
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
Semantic compositionality through recursive matrix-vector spaces
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Real, complex, and binary semantic vectors
QI'12 Proceedings of the 6th international conference on Quantum Interaction
An inference-based model of word meaning in context as a paraphrase distribution
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Hi-index | 0.01 |
Modelling compositional meaning for sentences using empirical distributional methods has been a challenge for computational linguists. We implement the abstract categorical model of Coecke et al. (2010) using data from the BNC and evaluate it. The implementation is based on unsupervised learning of matrices for relational words and applying them to the vectors of their arguments. The evaluation is based on the word disambiguation task developed by Mitchell and Lapata (2008) for intransitive sentences, and on a similar new experiment designed for transitive sentences. Our model matches the results of its competitors in the first experiment, and betters them in the second. The general improvement in results with increase in syntactic complexity showcases the compositional power of our model.