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dc.contributor.authorAltszyler, E-
dc.contributor.authorRibeiro, S-
dc.contributor.authorSigman, M-
dc.contributor.authorFernández Slezak, D-
dc.date.accessioned2017-11-03T12:21:28Z-
dc.date.available2017-11-03T12:21:28Z-
dc.date.issued2017-09-21-
dc.identifier.urihttps://repositorio.ufrn.br/jspui/handle/123456789/24164-
dc.languageengpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectDream content analysispt_BR
dc.subjectWord2vecpt_BR
dc.subjectLatent Semantic Analysispt_BR
dc.titleThe interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of textpt_BR
dc.typearticlept_BR
dc.identifier.doihttps://doi.org/10.1016/j.concog.2017.09.004-
dc.description.resumoComputer-based dreams content analysis relies on word frequencies within predefined categories in order to identify different elements in text. As a complementary approach, we explored the capabilities and limitations of word-embedding techniques to identify word usage patterns among dream reports. These tools allow us to quantify words associations in text and to identify the meaning of target words. Word-embeddings have been extensively studied in large datasets, but only a few studies analyze semantic representations in small corpora. To fill this gap, we compared Skip-gram and Latent Semantic Analysis (LSA) capabilities to extract semantic associations from dream reports. LSA showed better performance than Skip-gram in small size corpora in two tests. Furthermore, LSA captured relevant word associations in dream collection, even in cases with low-frequency words or small numbers of dreams. Word associations in dreams reports can thus be quantified by LSA, which opens new avenues for dream interpretation and decoding.pt_BR
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