The present paper addresses the study of cross-linguistic phonosemantic correspondences within a deep learning 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 and cross-family vector space. The processing network is then tested, without further training, in a language that does not appear in the training set and belongs to a different language family. The performance of the model is evaluated through a comparison with a monolingual and mono-family upper bound and a randomized baseline. After the assessment of the network's performance, the distribution of phonosemantic properties in the lexicon is inspected in relation to different (psycho)linguistic variables, showing a link between lexical non-arbitrariness and semantic, syntactic, pragmatic, and developmental factors.

A Layered Bridge from Sound to Meaning: Investigating Cross-linguistic Phonosemantic Correspondences / de Varda, Andrea; Strapparava, Carlo. - 43:(2021), pp. 1029-1035. (Intervento presentato al convegno 43rd annual meeting of the of the Cognitive Science Society (CogSci 2021) tenutosi a Vienna, Austria nel 26 – 29 July 2021).

A Layered Bridge from Sound to Meaning: Investigating Cross-linguistic Phonosemantic Correspondences

Carlo Strapparava
2021-01-01

Abstract

The present paper addresses the study of cross-linguistic phonosemantic correspondences within a deep learning 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 and cross-family vector space. The processing network is then tested, without further training, in a language that does not appear in the training set and belongs to a different language family. The performance of the model is evaluated through a comparison with a monolingual and mono-family upper bound and a randomized baseline. After the assessment of the network's performance, the distribution of phonosemantic properties in the lexicon is inspected in relation to different (psycho)linguistic variables, showing a link between lexical non-arbitrariness and semantic, syntactic, pragmatic, and developmental factors.
2021
Proceedings of the 43rd annual meeting of the of the Cognitive Science Society (CogSci 2021)
USA
Cognitive Science Society
de Varda, Andrea; Strapparava, Carlo
A Layered Bridge from Sound to Meaning: Investigating Cross-linguistic Phonosemantic Correspondences / de Varda, Andrea; Strapparava, Carlo. - 43:(2021), pp. 1029-1035. (Intervento presentato al convegno 43rd annual meeting of the of the Cognitive Science Society (CogSci 2021) tenutosi a Vienna, Austria nel 26 – 29 July 2021).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/342812
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