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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, LOUHI@EMNLP 2020, Online, November 20, 2020. Holderness, Eben; Jimeno Yepes, Antonio; Lavelli, Alberto. - : HAL CCSD, 2020 In: https://halshs.archives-ouvertes.fr/halshs-03102506 ; Association for Computational Linguistics 2020. 2020, 978-1-952148-81-1 ; https://www.aclweb.org/anthology/volumes/2020.louhi-1/ (2020) BASE Show details 2 COVID-19 Twitter Monitor: Aggregating and Visualizing COVID-19 Related Trends in Social Media ... The 28th International Conference on Computational Linguistics 2020; Cornelius, Joseph; Ellendorff, Tilia. - : Underline Science Inc., 2020 BASE Show details 3 SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces ... Vani, K; Mitrovic, Sandra; Antonucci, Alessandro; Rinaldi, Fabio. - : arXiv, 2020 Abstract: Lexical semantic change detection (also known as semantic shift tracing) is a task of identifying words that have changed their meaning over time. Unsupervised semantic shift tracing, focal point of SemEval2020, is particularly challenging. Given the unsupervised setup, in this work, we propose to identify clusters among different occurrences of each target word, considering these as representatives of different word meanings. As such, disagreements in obtained clusters naturally allow to quantify the level of semantic shift per each target word in four target languages. To leverage this idea, clustering is performed on contextualized (BERT-based) embeddings of word occurrences. The obtained results show that our approach performs well both measured separately (per language) and overall, where we surpass all provided SemEval baselines. ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2010.00857 https://dx.doi.org/10.48550/arxiv.2010.00857 BASE Hide details

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