Feature engineering for author profiling and identification: on the relevance of syntax and discourse
Author profiling and identification are two areas of data-driven computational linguistics that have gained a lot of relevance due to their potential applications in, e.g., forensic linguistic studies, marketing analysis, and historic/literary authorship verification. Author profiling aims to identi...
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| Tipo de documento: | tese |
| Estado: | Versão publicada |
| Data de publicação: | 2017 |
| País: | España |
| Recursos: | CBUC, CESCA |
| Repositório: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/404984 |
| Acesso em linha: | http://hdl.handle.net/10803/404984 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Author profiling Author identification Text classification Stylometry Gender identification Machine learning Natural language processing Syntax Discourse Feature engineering Perfilament d'autors Identificació d'autors Classificació de text Identificació de gènere Estilometría Aprenentatge automàtic Processat del llenguatge Sintaxis Discurs 62 |
| Resumo: | Author profiling and identification are two areas of data-driven computational linguistics that have gained a lot of relevance due to their potential applications in, e.g., forensic linguistic studies, marketing analysis, and historic/literary authorship verification. Author profiling aims to identify demographic traits of the authors, while author identification aims to identify the authors themselves by searching for distinctive linguistic patterns that distinguish them. The majority of approaches in the related work tends to focus on the content of the texts. We argue that focusing on structure rather than content can be more effective. The main focus of the thesis is thus on feature engineering, the development, evaluation and application of the feature set in the context of machine learning techniques to author profiling and identification. We prove the profiling potential of syntactic and iscourse features, which achieve state-of-the-art performance in many different scenarios, especially when combined with other features. |
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