Results 221 to 230 of about 15,278 (236)

SemEval-2007 task 14 [PDF]

Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007
The "Affective Text" task focuses on the classification of emotions and valence (positive/negative polarity) in news headlines, and is meant as an exploration of the connection between emotions and lexical semantics. In this paper, we describe the data set used in the evaluation and the results obtained by the participating systems.
Strapparava C, Mihalcea R
exaly   +4 more sources

SemEval-2018 Task 1: Affect in Tweets [PDF]

Proceedings of The 12th International Workshop on Semantic Evaluation, 2018
We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3.
Mohammad Salameh, Svetlana Kiritchenko, Saif M. Mohammad, Felipe Bravo-Marquez   +3 more
exaly   +3 more sources

SemEval-2014 Task 7: Analysis of Clinical Text [PDF]

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014
This paper describes the SemEval-2014, Task 7 on the Analysis of Clinical Text and presents the evaluation results. It focused on two subtasks: (i) identification (Task A) and (ii) normalization (Task B) of diseases and disorders in clinical reports as annotated in the Shared Annotated Resources (ShARe) 1 corpus.
Noémie Elhadad, Suresh Manandhar, Wendy W. Chapman, Sameer Pradhan, Guergana Savova, Sharon Lipsky Gorman   +5 more
openaire   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Computational semantic analysis of language: SemEval-2014 and beyond

Language Resources and Evaluation, 2016
Nakov, Preslav, Zesch, Torsten
openaire   +2 more sources

Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts

Language Resources and Evaluation, 2016
Preslav Nakov
exaly  

SICK through the SemEval glasses. Lesson learned from the evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment

Language Resources and Evaluation, 2016
Marco Marelli, Raffaella Bernardi, Roberto Zamparelli   +2 more
exaly  

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

2017
Eneko Agirre
exaly  

Target-Aspect-Sentiment Joint Detection for Aspect-Based Sentiment Analysis

Proceedings of the AAAI Conference on Artificial Intelligence, 2020
exaly  

SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours

2017
Kalina Bontcheva, Arkaitz Zubiaga
exaly  

SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter

2019
Valerio Basile, Cristina Bosco, Manuela Sanguinetti   +2 more
exaly