• Corpus ID: 52302913

Overview of TASS 2018: Opinions, Health and Emotions

@inproceedings{MartnezCmara2018OverviewOT,
  title={Overview of TASS 2018: Opinions, Health and Emotions},
  author={Eugenio Mart{\'i}nez-C{\'a}mara and Yudivi{\'a}n Almeida-Cruz and Manuel Carlos D{\'i}az-Galiano and Suilan Est{\'e}vez-Velarde and Miguel {\'A}ngel Garc{\'i}a Cumbreras and Manuel Garc{\'i}a Vega and Yoan Guti{\'e}rrez V{\'a}zquez and Arturo Montejo R{\'a}ez and Andr{\'e}s Montoyo and Rafael Mu{\~n}oz and Alejandro Piad-Morffis and Julio Villena-Rom{\'a}n},
  booktitle={TASS@SEPLN},
  year={2018}
}
This work has been partially supported by a grant from the Fondo Europeo de Desarrollo Regional (FEDER), the projects REDES (TIN2015-65136-C2-1-R, TIN2015-65136-C2-2-R) and SMART-DASCI (TIN2017-89517-P) from the Spanish Government, and “Plataforma Inteligente para Recuperacion, Analisis y Representacion de la Informacion Generada por Usuarios en Internet” (GRE16-01) from University of Alicante. Eugenio Martinez Camara was supported by the Spanish Government Programme Juan de la Cierva Formacion… 
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Highly Influential Citations
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Methods Citations
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Results Citations
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43 Citations

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References

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TLDR
The approaches used for “Good Or Bad News? Emotional categorization of news articles” task, the results obtained and a discussion of these results are described.
LABDA at TASS-2018 Task 3: Convolutional Neural Networks for Relation Classification in Spanish eHealth documents
TLDR
The participation of the LABDA team at the subtask of classification of relationships between two identified entities in electronic health documents written in Spanish using a Convolutional Neural Network with the word embedding and the position embedding to classify the type of the relation between two entities in the sentence.
SINAI en TASS 2018: Inserción de Conocimiento Emocional Externo a un Clasificador Lineal de Emociones (SINAI at TASS 2018: Lineal Classification System with Emotional External Knowledge)
TLDR
The emotion classification system developed by the SINAI team for the Task 4 at TASS 2018 workshop is presented, based on a supervised learning algorithm, SVM, using emotional features, which are grounded in several emotional lexicons.
ELiRF-UPV en TASS 2018: Análisis de Sentimientos en Twitter basado en Aprendizaje Profundo (ELiRF-UPV at TASS 2018: Sentiment Analysis in Twitter based on Deep Learning)
This paper describes the participation of the ELiRF research group of the Universitat Politècnica de València at TASS2018 Workshop which is a satellite event of the XXXIV edition of the International…
SINAI en TASS 2018 Task 3. Clasificando acciones y conceptos con UMLS en MedLine (SINAI in TASS 2018 Task 3. Classifying actions and concepts with UMLS on MedLine)
TLDR
The goal has been to develop a system for detecting medical entities in Spanish using Natural Language Processing techniques to finally classify terms into actions or concepts and the results obtained have been satisfactory although the classification needs to be improved.
SCI2S at TASS 2018: Emotion Classification with Recurrent Neural Networks
TLDR
The participation of the team SCI2S in all the Subtasks of the Task 4 of TASS 2018 is described and three Deep Learning models that are based on a sequence encoding layer built on a Long Short-Term Memory gated-architecture of Recurrent Neural Network are proposed.
Joint classification of key-phrases and relations in electronic health documents
Aplicación de un modelo híbrido de aprendizaje profundo para el Análisis de Sentimiento en Twitter(Application of a hybrid deep learning model for Sentiment Analysis in Twitter)
TLDR
This paper describes the participation of ITAINNOVA at sentiment analysis at Tweet level task within TASS 2018 workshop and a possible future work line which combines this architecture with the algorithm presented in the previous TASS edition.
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Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in data science.
Overview of TASS 2016
TLDR
The TASS 2016 proposed tasks, the description of the corpora used, the participant groups, the results and analysis of them are presented.
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