Event Knowledge in Compositional Distributional Semantics
The great majority of compositional models in distributional semantics present methods to compose vectors or tensors in a representation of the sentence. Here we propose to enrich one of the best performing methods (vector addition, which we take as a baseline) with distributional knowledge about ev...
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| Gepubliceerd in: | URI:https://journals.openedition.org/ijcol, |
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| Hoofdauteurs: | , |
| Formaat: | Article ou chapitre numérique |
| Taal: | Anglais |
| Gepubliceerd in: |
IJCoL
2020
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| Online toegang: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| Samenvatting: | The great majority of compositional models in distributional semantics present methods to compose vectors or tensors in a representation of the sentence. Here we propose to enrich one of the best performing methods (vector addition, which we take as a baseline) with distributional knowledge about events. The resulting model is able to outperform our baseline. |
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