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The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text
Open accessAbstract
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.
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“Previous work has shown that ASR accuracy is reduced for both elderly patients and those with dementia [99], [100], [101].”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [100]
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Metadata
- Authors
- Altszyler, E., Ribeiro, S., Sigman, M., & Fernández Slezak, D.
- Affiliations
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Universidade Federal do Rio Grande do Norte (BR)
- Corresponding author
- Altszyler, Edgar
- Venue
- Consciousness and Cognition
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 56
- Pages
- 178-187
- ISSN
- 1053-8100 (ISSN-L)
- OpenAlex
- W2529840421
- Cited by
- 128
- References
- 66
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 9 · 2023: 14 · 2022: 13 · 2021: 26 · 2020: 14 · 2019: 21 · 2018: 20 · 2017: 3
- FWCI
- 9.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Consciousness and Cognition (publishedVersion)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- El Servicio de Difusión de la Creación Intelectual (National University of La Plata) (submittedVersion, cc-by-sa)
- Americanae (AECID Library) (submittedVersion, other-oa)
- CONICET Digital (CONICET) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
- Universidad de Buenos Aires
- Agencia Nacional de Promoción Científica y Tecnológica
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Life Sciences › Neuroscience › Cognitive Neuroscience › Sleep and Wakefulness Research
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- dream interpretation, dream content analysis, latent semantic analysis, word embeddings, skip-gram, word association, small corpora, dream narrative, low-frequency words, semantic ambiguity
- MeSH terms
- Association, Dreams, Humans, Psycholinguistics, Semantics
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