Paper: Experimental Support for a Categorical Compositional Distributional Model of Meaning

ACL ID D11-1129
Title Experimental Support for a Categorical Compositional Distributional Model of Meaning
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

Modelling compositional meaning for sen- tences using empirical distributional methods has been a challenge for computational lin- guists. 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 devel- oped by Mitchell and Lapata (2008) for intran- sitive sentences, and on a similar new experi- ment 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 show- cases the compositional power o...