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Found 4 papers, 1 papers with code
A Comparative Study on Word Embeddings and Social NLP Tasks
In recent years, gray social media platforms, those with a loose moderation policy on cyberbullying, have been attracting more users.
SOS: Systematic Offensive Stereotyping Bias in Word Embeddings
Systematic Offensive stereotyping (SOS) in word embeddings could lead to associating marginalised groups with hate speech and profanity, which might lead to blocking and silencing those groups, especially on social media platforms.
FAMAC: A Federated Assisted Modified Actor-Critic Framework for Secured Energy Saving in 5G and Beyond Networks
The performance evaluation reveals that the proposed framework can achieve an energy saving that is about 77% more than that of the state-of-the-art solutions while respecting the QoS constraints of the network.
On Bias and Fairness in NLP: How to have a fairer text classification?
In this paper, we provide a holistic analysis of the different sources of bias, Upstream, Sample and Overampflication biases, in NLP models.
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