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pro vyhledávání: '"Periti, Francesco"'
Modern language models are capable of contextualizing words based on their surrounding context. However, this capability is often compromised due to semantic change that leads to words being used in new, unexpected contexts not encountered during pre
Externí odkaz:
http://arxiv.org/abs/2404.18570
Autor:
Periti, Francesco, Tahmasebi, Nina
Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC). Current evaluations typically focus on a specific task known as Graded Change Detection (GCD). However, performance comparison across work are often misleadi
Externí odkaz:
http://arxiv.org/abs/2402.12011
In the universe of Natural Language Processing, Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open research problems. In this paper, we specifically focus on the temporal probl
Externí odkaz:
http://arxiv.org/abs/2401.14040
Modern data mining applications require to perform incremental clustering over dynamic datasets by tracing temporal changes over the resulting clusters. In this paper, we propose A-Posteriori affinity Propagation (APP), an incremental extension of Af
Externí odkaz:
http://arxiv.org/abs/2401.14439
Autor:
Montanelli, Stefano, Periti, Francesco
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 scientists through manual and time-co
Externí odkaz:
http://arxiv.org/abs/2304.01666
Autor:
Curini, Luigi, Decadri, Silvia, Ferrara, Alfio, Montanelli, Stefano, Negri, Fedra, Periti, Francesco
Publikováno v:
Politics & Gender; Mar2024, Vol. 20 Issue 1, p182-211, 30p
Akademický článek
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