The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text

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....

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Detalles Bibliográficos
Autores principales:	Altszyler, E., Ribeiro, S., Sigman, M., Fernández Slezak, D.
Formato:	JOUR
Materias:	Dream content analysis Latent Semantic Analysis Word2vec content analysis controlled study dream embedding association human procedures psycholinguistics psychology semantics Association Dreams Humans Psycholinguistics Semantics
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_10538100_v56_n_p178_Altszyler
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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spelling	todo:paper_10538100_v56_n_p178_Altszyler2023-10-03T16:00:36Z The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text Altszyler, E. Ribeiro, S. Sigman, M. Fernández Slezak, D. Dream content analysis Latent Semantic Analysis Word2vec content analysis controlled study dream embedding association dream human procedures psycholinguistics psychology semantics Association Dreams Humans Psycholinguistics Semantics 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. © 2017 Elsevier Inc. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_10538100_v56_n_p178_Altszyler
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Dream content analysis Latent Semantic Analysis Word2vec content analysis controlled study dream embedding association dream human procedures psycholinguistics psychology semantics Association Dreams Humans Psycholinguistics Semantics
spellingShingle	Dream content analysis Latent Semantic Analysis Word2vec content analysis controlled study dream embedding association dream human procedures psycholinguistics psychology semantics Association Dreams Humans Psycholinguistics Semantics Altszyler, E. Ribeiro, S. Sigman, M. Fernández Slezak, D. The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text
topic_facet	Dream content analysis Latent Semantic Analysis Word2vec content analysis controlled study dream embedding association dream human procedures psycholinguistics psychology semantics Association Dreams Humans Psycholinguistics Semantics
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. © 2017 Elsevier Inc.
format	JOUR
author	Altszyler, E. Ribeiro, S. Sigman, M. Fernández Slezak, D.
author_facet	Altszyler, E. Ribeiro, S. Sigman, M. Fernández Slezak, D.
author_sort	Altszyler, E.
title	The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text
title_short	The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text
title_full	The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text
title_fullStr	The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text
title_full_unstemmed	The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text
title_sort	interpretation of dream meaning: resolving ambiguity using latent semantic analysis in a small corpus of text
url	http://hdl.handle.net/20.500.12110/paper_10538100_v56_n_p178_Altszyler
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The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text

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