The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text
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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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ASR accuracy is reduced for elderly patients and for patients with dementia.
“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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The Novel in the Spanish Silver Age
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Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic
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STMC: Semantic Tag Medical Concept Using Word2Vec Representation
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Analysis of the Word2Vec Model for Semantic Similarities in Indonesian Words
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Text semantic understanding based on knowledge enhancement and multi-granular feature extraction
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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