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EnglishThe study aims at comparing two methods for tracing the temporal evolution of topics and keywords in corpora of scientific literature: the well-known Latent Dirichelet Allocation and a new knowledge-based system that has been developed in a functional data analysis unsupervised perspective. Object of the study is a corpus of abstracts of articles published by the American Journal of Sociology over a century (1921-2018). Our study advocates that the two methods might not be seen as alternative but rather as integrable means to improve the interpretation of findings.
| Titolo: | Knowledge discovery for dynamic textual data: temporal patterns of topics and word clusters in corpora of scientific literature |
| Autori: | |
| Data di pubblicazione: | 2019 |
| Abstract: | The study aims at comparing two methods for tracing the temporal evolution of topics and keywords in corpora of scientific literature: the well-known Latent Dirichelet Allocation and a new knowledge-based system that has been developed in a functional data analysis unsupervised perspective. Object of the study is a corpus of abstracts of articles published by the American Journal of Sociology over a century (1921-2018). Our study advocates that the two methods might not be seen as alternative but rather as integrable means to improve the interpretation of findings. |
| Handle: | http://hdl.handle.net/11368/2951150 |
| ISBN: | 978-88-9191-510-8 |
| URL: | https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Dirigenti e istituzioni/ISTITUZIONI-HE-PDF-sis2019_V4.pdf |
| Appare nelle tipologie: | 4.1 Contributo in Atti Convegno (Proceeding) |
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| Trevisani_Knowledge discovery for dynamic textual data.pdf | contributo con frontespizio e indice del libro | Documento in Versione Editoriale | Copyright Editore | Administrator Richiedi una copia |
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