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Title: The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text
Authors: Altszyler, E
Ribeiro, Sidarta Tollendal Gomes
Sigman, M
Fernández Slezak, D
Keywords: Dream content analysis;Word2vec;Latent Semantic Analysis
Issue Date: 21-Sep-2017
Portuguese Abstract: Computer-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.
Appears in Collections:ICe - Artigos publicados em periódicos

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