Results 1 to 10 of about 18,781 (289)
SemEval-2016 Task 5: Aspect Based Sentiment Analysis [PDF]
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016 International audienceThis paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015.
S Senthilvelan +2 more
exaly +12 more sources
SemEval-2016 Task 6: Detecting Stance in Tweets [PDF]
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016 10th International Workshop on Semantic Evaluation (SemEval-2016), 16-17 June 2016, San Diego, California ...
Parinaz Sobhani
exaly +6 more sources
SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval) [PDF]
arXiv, 2023 We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan ...
Shamsuddeen Hassan Muhammad +9 more
arxiv +7 more sources
A semi supervised approach to Arabic aspect category detection using Bert and teacher-student model [PDF]
PeerJ Computer Science, 2023 Aspect-based sentiment analysis tasks are well researched in English. However, we find such research lacking in the context of the Arabic language, especially with reference to aspect category detection.
Miada Almasri +2 more
doaj +3 more sources
SemEval-2014 Task 9: Sentiment Analysis in Twitter [PDF]
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014 Sentiment analysis, microblog sentiment analysis, Twitter opinion mining, sarcasm, LiveJournal ...
Sara Rosenthal +3 more
openalex +4 more sources
Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions - DEW '09, 2009 In this paper, we describe the SemEval-2010 shared task on "Linking Events and Their Participants in Discourse". This task is a variant of the classical semantic role labelling task. The novel aspect is that we focus on linking local semantic argument structures across sentence boundaries.
Josef Ruppenhofer +4 more
openalex +5 more sources
Duluth at SemEval-2017 Task 6: Language Models in Humor Detection [PDF]
arXiv, 2017 This paper describes the Duluth system that participated in SemEval-2017 Task 6 #HashtagWars: Learning a Sense of Humor. The system participated in Subtasks A and B using N-gram language models, ranking highly in the task evaluation. This paper discusses the results of our system in the development and evaluation stages and from two post-evaluation ...
Pedersen, Ted, Yan, Xinru
arxiv +3 more sources
SemEval-2015 Task 6: Clinical TempEval [PDF]
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015 Clinical TempEval 2015 brought the temporal information extraction tasks of past TempEval campaigns to the clinical domain. Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification.
Steven Bethard +4 more
openalex +3 more sources
SemEval-2016 Task 12: Clinical TempEval [PDF]
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016 Clinical TempEval 2016 evaluated temporal information extraction systems on the clinical domain. Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were trained and evaluated on a corpus of clinical and pathology notes from the Mayo
Steven Bethard +5 more
openalex +3 more sources
SemEval-2016 Task 4: Sentiment Analysis in Twitter [PDF]
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016 This paper discusses the fourth year of the ``Sentiment Analysis in Twitter Task''. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new
Preslav Nakov, Fabrizio Sebastiani
exaly +7 more sources