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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 – 2 of 2 1 GM-CTSC at SemEval-2020 Task 1: Gaussian mixtures cross temporal similarity clustering Cassotti, Pierluigi; Caputo, Annalina; Polignano, Marco... In: Cassotti, Pierluigi, Caputo, Annalina orcid:0000-0002-7144-8545 , Polignano, Marco orcid:0000-0002-3939-0136 and Basile, Pierpaolo orcid:0000-0002-0545-1105 (2020) GM-CTSC at SemEval-2020 Task 1: Gaussian mixtures cross temporal similarity clustering. In: Fourteenth Workshop on Semantic Evaluation, Dec 2020, Barcelona (Online). (2020) BASE Show details 2 GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering ... Cassotti, Pierluigi; Caputo, Annalina; Polignano, Marco; Basile, Pierpaolo. - : arXiv, 2020 Abstract: This paper describes the system proposed for the SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. We focused our approach on the detection problem. Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or loosed senses. To this end, we defined a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods. We compared the proposed approach with a number of similarity-based thresholds. We found that, although the performance of the detection methods varies across the word embedding algorithms, the combination of Gaussian Mixture with Temporal Referencing resulted in our best system. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2005.09946 https://arxiv.org/abs/2005.09946 BASE Hide details

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