Semantic Shift Detection
5 papers with code • 0 benchmarks • 0 datasets
Detect the semantic change of a word between two corpora
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Most implemented papers
A Survey on Contextualised Semantic Shift Detection
Semantic Shift Detection (SSD) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word.
ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms
princetonvisualai/imagenetood
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Through comprehensive experiments, we show that OOD detectors are more sensitive to covariate shift than to semantic shift, and the benefits of recent OOD detection algorithms on semantic shift detection is minimal.
Function-Space Regularization in Neural Networks: A Probabilistic Perspective
timrudner/function-space-empirical-bayes
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In this work, we approach regularization in neural networks from a probabilistic perspective and show that by viewing parameter-space regularization as specifying an empirical prior distribution over the model parameters, we can derive a probabilistically well-motivated regularization technique that allows explicitly encoding information about desired predictive functions into neural network training.
Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck
hlidmhkust/VCCIB
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Specifically, we propose an invariant feature encoding approach based on the IB principle and IRM framework for domainshift generalization, which aims to find the causal relationship between the input data and task result by minimizing the complexity and domain dependence of the encoded feature.
Historical Ink: Semantic Shift Detection for 19th Century Spanish
This paper explores the evolution of word meanings in 19th-century Spanish texts, with an emphasis on Latin American Spanish, using computational linguistics techniques.
