TASS 2018: The Strength of Deep Learning in Language Understanding Tasks

@article{DazGaliano2019TASS2T,
  title={TASS 2018: The Strength of Deep Learning in Language Understanding Tasks},
  author={Manuel Carlos D{\'i}az-Galiano and Miguel {\'A}ngel Garc{\'i}a Cumbreras and Manuel Garc{\'i}a Vega and Yoan Guti{\'e}rrez-V{\'a}zquez and Eugenio Mart{\'i}nez-C{\'a}mara and Alejandro Piad-Morffis and Julio Villena-Rom{\'a}n},
  journal={Proces. del Leng. Natural},
  year={2019},
  volume={62},
  pages={77-84}
}
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… 

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