| Téléchargement | - Voir la version finale : End-to-end multi-view networks for text classification (PDF, 946 Kio)
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| Auteur | Rechercher : Guo, Hongyu1; Rechercher : Cherry, Colin1; Rechercher : Su, Jiang1 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies de l'information et des communications
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| Format | Texte, Article |
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| Sujet | computation and language; learning; neural and evolutionary computing |
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| Résumé | We propose a multi-view network for text classification. Our method automatically creates various views of its input text, each taking the form of soft attention weights that distribute the classifier's focus among a set of base features. For a bag-of-words representation, each view focuses on a different subset of the text's words. Aggregating many such views results in a more discriminative and robust representation. Through a novel architecture that both stacks and concatenates views, we produce a network that emphasizes both depth and width, allowing training to converge quickly. Using our multi-view architecture, we establish new state-of-the-art accuracies on two benchmark tasks. |
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| Date de publication | 2017-04-19 |
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| Maison d’édition | Cornell University Library |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Non |
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| Numéro NPARC | 23002277 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 0c6ea103-6166-460f-90ac-06eff86608d0 |
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| Enregistrement créé | 2017-09-28 |
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| Enregistrement modifié | 2020-05-30 |
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