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

被引：5

作者：

Carlos Diaz-Galiano, Manuel ^{[1
]}

Garcia-Cumbreras, Miguel A. ^{[1
]}

Garcia-Vega, Manuel ^{[1
]}

Gutierrez, Yoan ^{[2
]}

Martinez-Camara, Eugenio ^{[3
]}

Piad-Morffis, Alejandro ^{[4
]}

Villena-Roman, Julio ^{[5
]}

机构：

[1] Univ Jaen, CEATIC, Jaen, Spain

[2] Univ Alicante, Alicante, Spain

[3] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada, Spain

[4] Univ Havana, Havana, Cuba

[5] MeaningCloud, Madrid, Spain

来源：

PROCESAMIENTO DEL LENGUAJE NATURAL | 2019年 / 62期

关键词：

Sentiment analysis; emotion classification; digital health; AGREEMENT;

D O I：

10.26342/2019-62-9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：77 / 84

页数：8

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