The present paper aims to investigate the nature and the extent of cross-linguistic phonosemantic correspondences within a computational framework. An LSTM-based Recurrent Neural Network is trained to associate the phonetic representation of a word, encoded as a sequence of feature vectors, to its corresponding semantic representation in a multilingual vector space. The processing network is tested, without further training, in a language that does not appear in the training set. The performance of the multilingual model is compared with a monolingual upper bound and a randomized baseline. After the quantitative evaluation of its performance, a qualitative analysis is carried out on the network’s most effective predictions, showing an inhomogeneous distribution of phonosemantic information in the lexicon, influenced by semantic, syntactic, and pragmatic factors.
Phonological layers of meaning: A computational exploration of sound iconicity / de Varda, A. G.; Strapparava, C.. - 2769:(2020). ((Intervento presentato al convegno 7th Italian Conference on Computational Linguistics, CLiC-it 2020 tenutosi a ita nel 2021.
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Titolo: | Phonological layers of meaning: A computational exploration of sound iconicity | |
Autori: | de Varda, A. G.; Strapparava, C. | |
Autori Unitn: | ||
Titolo del volume contenente il saggio: | CEUR Workshop Proceedings | |
Luogo di edizione: | Germany | |
Casa editrice: | CEUR-WS | |
Anno di pubblicazione: | 2020 | |
Codice identificativo Scopus: | 2-s2.0-85097862232 | |
Handle: | http://hdl.handle.net/11572/341942 | |
Citazione: | Phonological layers of meaning: A computational exploration of sound iconicity / de Varda, A. G.; Strapparava, C.. - 2769:(2020). ((Intervento presentato al convegno 7th Italian Conference on Computational Linguistics, CLiC-it 2020 tenutosi a ita nel 2021. | |
Appare nelle tipologie: | 04.1 Saggio in atti di convegno (Paper in proceedings) |