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....
| Publicado: |
2017
|
|---|---|
| Materias: | |
| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10538100_v56_n_p178_Altszyler http://hdl.handle.net/20.500.12110/paper_10538100_v56_n_p178_Altszyler |
| Aporte de: |
| id |
paper:paper_10538100_v56_n_p178_Altszyler |
|---|---|
| record_format |
dspace |
| spelling |
paper:paper_10538100_v56_n_p178_Altszyler2023-06-08T16:03:01Z The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text 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. 2017 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10538100_v56_n_p178_Altszyler 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 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. |
| 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 |
| publishDate |
2017 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10538100_v56_n_p178_Altszyler http://hdl.handle.net/20.500.12110/paper_10538100_v56_n_p178_Altszyler |
| _version_ |
1768543433069166592 |