Refining the Distributional Inclusion Hypothesis for Unsupervised Hypernym Identification
Several unsupervised methods for hypernym detection have been investigated in distributional semantics. Here we present a new approach based on a smoothed version of the distributional inclusion hypothesis. The new method is able to improve hypernym detection after testing on the BLESS dataset.
Enregistré dans:
| Dans: | URI:https://journals.openedition.org/ijcol, |
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| Auteurs principaux: | , , |
| Format: | Article ou chapitre numérique |
| Langue: | Anglais |
| Publié: |
IJCoL
2020
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| Accès en ligne: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| Résumé: | Several unsupervised methods for hypernym detection have been investigated in distributional semantics. Here we present a new approach based on a smoothed version of the distributional inclusion hypothesis. The new method is able to improve hypernym detection after testing on the BLESS dataset. |
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