Papers Semantic Shift Detection
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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. It addresses the Semantic Shift Detection (SSD) ta…
Masked Language ModelingSemantic Shift DetectionSemantic SimilarityTackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck
Task-oriented communication aims to extract and transmit task-relevant information to significantly reduce the communication overhead and transmission latency. However, the unpredictable distribution shifts between train…
image-classificationImage ClassificationSemantic Shift DetectionAttacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks
Adversarial examples have been shown to cause neural networks to fail on a wide range of vision and language tasks, but recent work has claimed that Bayesian neural networks (BNNs) are inherently robust to adversarial pe…
Adversarial RobustnessPredictionSemantic Shift DetectionFunction-Space Regularization in Neural Networks: A Probabilistic Perspective
Parameter-space regularization in neural network optimization is a fundamental tool for improving generalization. However, standard parameter-space regularization methods make it challenging to encode explicit preference…
Semantic Shift DetectionMindful Explanations: Prevalence and Impact of Mind Attribution in XAI Research
When users perceive AI systems as mindful, independent agents, they hold them responsible instead of the AI experts who created and designed these systems. So far, it has not been studied whether explanations support thi…
ArticlesSemantic Shift DetectionImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms
The task of out-of-distribution (OOD) detection is notoriously ill-defined. Earlier works focused on new-class detection, aiming to identify label-altering data distribution shifts, also known as "semantic shift." Howeve…
Out-of-Distribution DetectionOut of Distribution (OOD) DetectionSemantic Shift DetectionA 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. Traditionally, SSD has been addressed by linguists and social scienti…
Semantic Shift DetectionSurveyWord EmbeddingsA Methodology for Building a Diachronic Dataset of Semantic Shifts and its Application to QC-FR-Diac-V1.0, a Free Reference for French
Different algorithms have been proposed to detect semantic shifts (changes in a word meaning over time) in a diachronic corpus. Yet, and somehow surprisingly, no reference corpus has been designed so far to evaluate them…
Semantic Shift DetectionWord Sense DisambiguationWhat is Done is Done: an Incremental Approach to Semantic Shift Detection
Contextual word embedding techniques for semantic shift detection are receiving more and more attention. In this paper, we present What is Done is Done (WiDiD), an incremental approach to semantic shift detection based o…
ClusteringSemantic Shift DetectionLeveraging Contextual Embeddings for Detecting Diachronic Semantic Shift
We propose a new method that leverages contextual embeddings for the task of diachronic semantic shift detection by generating time specific word representations from BERT embeddings. The results of our experiments in th…
Domain AdaptationSemantic Shift Detection