- Abstract:
-
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. (arXiv:1003.4394v1 [cs.CL]) 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 (200...
Expand abstract - Publication status:
- Published
- Journal:
- Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (2011)
- Volume:
- abs/1106.4058
- Pages:
- 1394-1404
- Publication date:
- 2011
- URN:
-
uuid:71c9f5a4-ab18-4cbd-8a5e-279ddd64287b
- Source identifiers:
-
305758
- Local pid:
- pubs:305758
- Keywords:
- Copyright date:
- 2011
- Notes:
-
11 pages, to be presented at EMNLP 2011, to be published in
Proceedings of the 2011 Conference on Empirical Methods in Natural Language
Processing
Journal article
Experimental Support for a Categorical Compositional Distributional Model of Meaning
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