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Inferring semantic representations from data| old_uid | 1245 |
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| title | Inferring semantic representations from data |
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| start_date | 2006/05/17 |
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| schedule | 15h45 |
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| online | no |
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| location_info | Wolfson House, Haldane Lecture Theatre |
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| summary | The talk addresses the question of how effectively we can learn underlying semantics from data. We concentrate on text analysis as a domain where semantics are relatively cleanly defined and on which learning approaches have made significant advances. The links between Latent Semantic Indexing, Latent Semantic Kernels and kernel Principal Components Analysis are discussed and the generalisation of such representations is discussed. Cross-lingual information retrieval suggests the use of Canonical Correlation Analysis as a Semantic
inference tool. Again a kernel version can be defined and with appropriate regularisation applied in high-dimensional feature spaces. Applications of the same approach to non-text data will also be presented. |
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| responsibles | Dayan |
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