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SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
TLDR
The results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval), based on a new dataset, contain over 14,000 English tweets, are presented. Expand
SemEval-2013 Task 2: Sentiment Analysis in Twitter
TLDR
Crowdourcing on Amazon Mechanical Turk was used to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks, which included two subtasks: A, an expression-level subtask, and B, a message level subtask. Expand
SemEval-2016 Task 4: Sentiment Analysis in Twitter
TLDR
The fourth year of the SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions, and the task continues to be very popular, attracting a total of 43 teams. Expand
Predicting the Type and Target of Offensive Posts in Social Media
TLDR
The Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, is complied and made publicly available. Expand
SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals
TLDR
A new task is introduced, which will be part of SemEval-2010: multi-way classification of mutually exclusive semantic relations between pairs of common nominals. Expand
SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals
TLDR
This paper defines the task, describes the training and test data and the process of their creation, lists the participating systems (10 teams, 28 runs), and discusses their results. Expand
SemEval-2015 Task 10: Sentiment Analysis in Twitter
TLDR
The 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. Expand
SemEval-2014 Task 9: Sentiment Analysis in Twitter
TLDR
The Sentiment Analysis in Twitter task is described, a continuation of the last year’s task that ran successfully as part of SemEval2013 and introduced three new test sets: regular tweets, sarcastic tweets, and LiveJournal sentences. Expand
Fine-Grained Analysis of Propaganda in News Article
TLDR
A novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type is proposed, and a novel multi-granularity neural network is designed that outperforms several strong BERT-based baselines. Expand
Overview of BioCreative II gene mention recognition
TLDR
It is demonstrated that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions. Expand
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