TASS 2018The Strength of Deep Learning in Language Understanding Tasks

  1. Manuel Carlos Díaz Galiano
  2. Miguel Ángel García Cumbreras
  3. Manuel García Vega
  4. Yoan Gutiérrez
  5. Eugenio Martínez Cámara
  6. Alejandro Piad-Morffis
  7. Julio Villena Román
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2019

Número: 62

Páginas: 77-84

Tipo: Artículo

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Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

The edition of TASS in 2018 was the edition of the evolution of TASS to a competitive evaluation workshop on semantic and text understanding tasks. Consequently, TASS has enlarged the coverage of tasks, and it goes beyond sentiment analysis. Thereby, two new tasks focused on semantic relation extraction in the health domain and emotion classification in the news domain were added to the two traditional tasks of TASS, namely sentiment analysis at tweet level and aspect level. Several systems were submitted, and most of them are based on state of the art classification methods, which highlight those ones grounded in Deep Learning. As addition contribution, TASS 2018 released two new corpora, specifically the ones of the two new tasks.

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Información de financiación

This work has been partially supported by a grant from the Fondo Europeo de De-sarrollo Regional (FEDER), the projects REDES (TIN2015-65136-C2-1-R, TIN2015-65136-C2-2-R), PROMETEU/2018/089 and SMART-DASCI (TIN2017-89517-P) from the Spanish Government. Eugenio Martínez Cámara was supported by the Spanish Government Programme Juan de la Cierva For-mación (FJCI-2016-28353).

Financiadores

    • TIN2015-65136-C2-1-R
    • FJCI-2016-28353

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