A deep learning perspective on linguistic ambiguity
This thesis studies the information that an expression and its context contribute to ambiguity resolution, focusing on the syntactic, lexical, and referential levels, and on the English language. I adopt computational linguistics, in particular deep learning methods, as my research framework, by int...
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| Formato: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Recursos: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/674202 |
| Acesso em linha: | http://hdl.handle.net/10803/674202 |
| Access Level: | acceso abierto |
| Palavra-chave: | Linguistic ambiguity 81 |
| Resumo: | This thesis studies the information that an expression and its context contribute to ambiguity resolution, focusing on the syntactic, lexical, and referential levels, and on the English language. I adopt computational linguistics, in particular deep learning methods, as my research framework, by introducing methodologies to 1) analyze deep learning models, and 2) use them for linguistic analysis. In a subset of studies, I investigate how neural language models – trained from unlabeled text corpora – process ambiguities, analyzing both their internal representations and their predictions. Other experiments employ deep learning models to estimate different kinds of linguistic information, with the goal of scaling the testing of linguistic hypotheses. Concretely, I study how an expression and its context interact during both interpretation and production. The thesis provides a comprehensive perspective on linguistic ambiguity, contributing to the understanding of the mechanisms that humans and artificial systems adopt to deal with this phenomenon. |
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