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Found 7 papers, 1 papers with code
Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot Annotator Adaptation
In subjective NLP tasks, where a single ground truth does not exist, the inclusion of diverse annotators becomes crucial as their unique perspectives significantly influence the annotations.
A Multi-Perspective Machine Learning Approach to Evaluate Police-Driver Interaction in Los Angeles
Interactions between the government officials and civilians affect public wellbeing and the state legitimacy that is necessary for the functioning of democratic society.
Towards a Unified Framework for Adaptable Problematic Content Detection via Continual Learning
Detecting problematic content, such as hate speech, is a multifaceted and ever-changing task, influenced by social dynamics, user populations, diversity of sources, and evolving language.
The Moral Foundations Reddit Corpus
Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, pro-environmental action, political engagement, and even participation in violent protests.
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian
Active Learning models can achieve the baseline performance (the accuracy of the model trained on the whole dataset), with a considerably lower amount of labeled data.
Irony Detection in Persian Language: A Transfer Learning Approach Using Emoji Prediction
In the current research, which is the first attempt at irony detection in Persian language, emoji prediction is used to build a pretrained model.
Twitter Trend Extraction: A Graph-based Approach for Tweet and Hashtag Ranking, Utilizing No-Hashtag Tweets
In order to overcome this issue and extract trends using all tweets, we propose a graph-based approach where graph nodes represent tweets as well as words and hashtags.
