| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560470 |
| ISSN: | 1405-5546 |
| Autores: | Etcheverry, Mathias1 Wonsever, Dina1 |
| Instituciones: | 1Universidad de la República, Montevideo. Uruguay |
| Año: | 2020 |
| Periodo: | Abr-Jun |
| Volumen: | 24 |
| Número: | 2 |
| Paginación: | 565-574 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | In this work we present a supervised approach to partially order word embeddings, through a learned order embedding, and we apply it in supervised hypernymy detection. We use neural network as an order embedding to map general purpose word embeddings to a partially ordered vector set. The mapping is trained using positive and negative instances of the relationship. We consider two alternatives to deal with compound terms: a character based embedding of an underscored version of the terms, and a convolutional neural network that consumes the word embedding of each term. We show that this distributional approach presents interesting results in comparison to other distributional and path-based approaches. In addition, we observe still good behavior on different sized portions of the training data. This may suggest an interesting generalization capability. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Hypernymy, Word embedding, Order embedding, Neural network, Siamese network, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
