| dc.creator | Altszyler, E | |
| dc.creator | Ribeiro, Sidarta Tollendal Gomes | |
| dc.creator | Sigman, M | |
| dc.creator | Fernández Slezak, D | |
| dc.date | 2017-11-03T12:21:28Z | |
| dc.date | 2017-11-03T12:21:28Z | |
| dc.date | 2017-09-21 | |
| dc.identifier | https://repositorio.ufrn.br/jspui/handle/123456789/24164 | |
| dc.identifier | https://doi.org/10.1016/j.concog.2017.09.004 | |
| dc.description | 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. | |
| dc.language | eng | |
| dc.subject | Dream content analysis | |
| dc.subject | Word2vec | |
| dc.subject | Latent Semantic Analysis | |
| dc.title | The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text | |
| dc.type | article | |
