In this paper we define the creativity embedding of a text based on four self-assessment creativity metrics, namely diversity, novelty, serendipity and magnitude, knowledge graphs, and neural networks. We use as basic unit the notion of triple (head, relation, tail). We investigate if additional information about creativity improves natural language processing tasks. In this work, we focus on triple plausibility task, exploiting BERT model and a WordNet11 dataset sample. Contrary to our hypothesis, we do not detect increase in the performance.

Creativity embedding: A vector to characterise and classify plausible triples in deep learning NLP models / Oliveri, I.; Ardito, L.; Rizzo, G.; Morisio, M.. - ELETTRONICO. - 2769:(2020), pp. 1-6. ((Intervento presentato al convegno 7th Italian Conference on Computational Linguistics, CLiC-it 2020 tenutosi a Bologna nel 2021.

Creativity embedding: A vector to characterise and classify plausible triples in deep learning NLP models

Oliveri I.;Ardito L.;Morisio M.
2020

Abstract

In this paper we define the creativity embedding of a text based on four self-assessment creativity metrics, namely diversity, novelty, serendipity and magnitude, knowledge graphs, and neural networks. We use as basic unit the notion of triple (head, relation, tail). We investigate if additional information about creativity improves natural language processing tasks. In this work, we focus on triple plausibility task, exploiting BERT model and a WordNet11 dataset sample. Contrary to our hypothesis, we do not detect increase in the performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2859116