Guillermo Jorge‐Botana
Biographic Data
| ID | 6937685 |
|---|---|
| NAME | Guillermo Jorge‐Botana |
| GIVEN NAMES | Guillermo |
| FAMILY NAME | Jorge‐Botana |
| SIGNATURE | BOTANA G J |
| AFFILIATIONS | Universidad Complutense de Madrid |
| ORCID | 0000-0001-5879-6783 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Are valence and arousal related to the development of amodal representations of words? A computational study
In this study, we analyzed the relationship between the amodal (semantic) development of words and two popular emotional norms (emotional valence and arousal) in English and Spanish languages. To do so, we combined the strengths of semantics from vector space models (vector length, semantic diversity, and word maturity measures), and feature-based models of emotions. First, we generated a common vector space representing the meaning of words at d…
Quantifying the ideational context: Political frames, meaning trajectories and punctuated equilibria in Spanish mainstream press during the Catalan nationalist challenge
This article presents a quantitative method for mapping semantic spaces and tracing political frames' trajectories, that facilitate the analysis of the connections between changes in ideas and sociopolitical phenomena. We test our approach in Spain, where the Catalan conflict fostered a competition in terms of decontestation of meanings of key political concepts. Using unsupervised machine learning, we track the salience, level of semantic fragme…
Predicting Word Maturity from Frequency and Semantic Diversity: A Computational Study
Semantic word representation changes over different ages of childhood until it reaches its adult form. One method to formally model this change is the word maturity paradigm. This method uses a text sample for each age, including adult age, and transforms the samples into a semantic space by means of Latent Semantic Analysis. The representation of a word at every age is then compared with its adult representation via computational maturity indice…
Transforming Selected Concepts Into Dimensions in Latent Semantic Analysis
This study presents a new approach for transforming the latent representation derived from a Latent Semantic Analysis (LSA) space into one where dimensions have nonlatent meanings. These meanings are based on lexical descriptors, which are selected by the LSA user. The authors present three analyses that provide examples of the utility of this methodology. The first analysis demonstrates how document terms can be projected into meaningful new dim…
Are valence and arousal related to the development of amodal representations of words? A computational study
In this study, we analyzed the relationship between the amodal (semantic) development of words and two popular emotional norms (emotional valence and arousal) in English and Spanish languages. To do so, we combined the strengths of semantics from vector space models (vector length, semantic diversity, and word maturity measures), and feature-based models of emotions. First, we generated a common vector space representing the meaning of words at d…
Predicting Word Maturity from Frequency and Semantic Diversity: A Computational Study
Semantic word representation changes over different ages of childhood until it reaches its adult form. One method to formally model this change is the word maturity paradigm. This method uses a text sample for each age, including adult age, and transforms the samples into a semantic space by means of Latent Semantic Analysis. The representation of a word at every age is then compared with its adult representation via computational maturity indice…
Transforming Selected Concepts Into Dimensions in Latent Semantic Analysis
This study presents a new approach for transforming the latent representation derived from a Latent Semantic Analysis (LSA) space into one where dimensions have nonlatent meanings. These meanings are based on lexical descriptors, which are selected by the LSA user. The authors present three analyses that provide examples of the utility of this methodology. The first analysis demonstrates how document terms can be projected into meaningful new dim…
Predicting Word Maturity from Frequency and Semantic Diversity: A Computational Study
Semantic word representation changes over different ages of childhood until it reaches its adult form. One method to formally model this change is the word maturity paradigm. This method uses a text sample for each age, including adult age, and transforms the samples into a semantic space by means of Latent Semantic Analysis. The representation of a word at every age is then compared with its adult representation via computational maturity indice…
Quantifying the ideational context: Political frames, meaning trajectories and punctuated equilibria in Spanish mainstream press during the Catalan nationalist challenge
This article presents a quantitative method for mapping semantic spaces and tracing political frames' trajectories, that facilitate the analysis of the connections between changes in ideas and sociopolitical phenomena. We test our approach in Spain, where the Catalan conflict fostered a competition in terms of decontestation of meanings of key political concepts. Using unsupervised machine learning, we track the salience, level of semantic fragme…
Are valence and arousal related to the development of amodal representations of words? A computational study
In this study, we analyzed the relationship between the amodal (semantic) development of words and two popular emotional norms (emotional valence and arousal) in English and Spanish languages. To do so, we combined the strengths of semantics from vector space models (vector length, semantic diversity, and word maturity measures), and feature-based models of emotions. First, we generated a common vector space representing the meaning of words at d…
Artificial Intelligence (3 works) · Computer Science (3 works) · Latent semantic analysis (3 works) · Linguistics (2 works) · Mathematics (2 works) · Natural language processing (2 works) · Natural Language Processing Techniques (2 works) · Psychology (2 works) · Representation (politics (2 works) · Topic Modeling (2 works)