Efficient strategies for hierarchical text classification: External knowledge and auxiliary tasks

Kervy Rivas Rojas, Gina Bustamante, Arturo Oncevay, Marco A. Sobrevilla Cabezudo

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13 Citas (Scopus)

Resumen

In hierarchical text classification, we perform a sequence of inference steps to predict the category of a document from top to bottom of a given class taxonomy. Most of the studies have focused on developing novels neural network architectures to deal with the hierarchical structure, but we prefer to look for efficient ways to strengthen a baseline model. We first define the task as a sequence-to-sequence problem. Afterwards, we propose an auxiliary synthetic task of bottom-up-classification. Then, from external dictionaries, we retrieve textual definitions for the classes of all the hierarchy's layers, and map them into the word vector space. We use the class-definition embeddings as an additional input to condition the prediction of the next layer and in an adapted beam search. Whereas the modified search did not provide large gains, the combination of the auxiliary task and the additional input of class-definitions significantly enhance the classification accuracy. With our efficient approaches, we outperform previous studies, using a drastically reduced number of parameters, in two well-known English datasets.

Idioma originalInglés
Título de la publicación alojadaACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
EditorialAssociation for Computational Linguistics (ACL)
Páginas2252-2257
Número de páginas6
ISBN (versión digital)9781952148255
EstadoPublicada - 2020
Publicado de forma externa
Evento58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, Estados Unidos
Duración: 5 jul. 202010 jul. 2020

Serie de la publicación

NombreProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (versión impresa)0736-587X

Conferencia

Conferencia58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
País/TerritorioEstados Unidos
CiudadVirtual, Online
Período5/07/2010/07/20

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