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1
The corpora they are a-changing: a case study in Italian newspapers
In: Basile, Pierpaolo orcid:0000-0002-0545-1105 , Caputo, Annalina orcid:0000-0002-7144-8545 , Caselli, Tommaso orcid:0000-0003-2936-0256 , Cassotti, Pierluigi and Varvara, Rossella orcid:0000-0001-9957-2807 (2021) The corpora they are a-changing: a case study in Italian newspapers. In: 2nd International Workshop on Computational Approaches to Historical Language Change 2021, Online. (2021)
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2
Extracting Relations from Italian Wikipedia using Self-Training ...
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3
Extracting Relations from Italian Wikipedia using Self-Training ...
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4
Extracting Relations from Italian Wikipedia using Self-Training ...
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5
DIACR-Ita @ EVALITA2020: overview of the EVALITA2020 DiachronicLexical semantics (DIACR-Ita) task
In: Basile, Pierpaolo, Caputo, Annalina orcid:0000-0002-7144-8545 , Caselli, Tommaso orcid:0000-0003-2936-0256 , Cassotti, Pierluigi and Varvara, Rossella orcid:0000-0001-9957-2807 (2020) DIACR-Ita @ EVALITA2020: overview of the EVALITA2020 DiachronicLexical semantics (DIACR-Ita) task. In: Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, 17 Dec 2020, Online. (2020)
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6
A diachronic Italian corpus based on “L’Unit`a”
In: Basile, Pierpaolo, Caputo, Annalina orcid:0000-0002-7144-8545 , Caselli, Tommaso orcid:0000-0003-2936-0256 , Cassotti, Pierluigi and Varvara, Rossella orcid:0000-0001-9957-2807 (2020) A diachronic Italian corpus based on “L’Unit`a”. In: Seventh Italian Conference on Computational Linguistics, 1-3 Mar 2021, Bologna (Online). (2020)
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7
GM-CTSC at SemEval-2020 Task 1: Gaussian mixtures cross temporal similarity clustering
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)
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8
GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering ...
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
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