Jorge Carrillo‐de‐Albornoz
About
In The Last Decade
Jorge Carrillo‐de‐Albornoz
25 papers receiving 353 citations
Peers
Comparison fields: 5 of 56
- Artificial Intelligence 324
- Information Systems 74
- Sociology and Political Science 72
- Molecular Biology 39
- Social Psychology 32
Countries citing papers authored by Jorge Carrillo‐de‐Albornoz
This map shows the geographic impact of Jorge Carrillo‐de‐Albornoz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jorge Carrillo‐de‐Albornoz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jorge Carrillo‐de‐Albornoz more than expected).
Fields of papers citing papers by Jorge Carrillo‐de‐Albornoz
This network shows the impact of papers produced by Jorge Carrillo‐de‐Albornoz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jorge Carrillo‐de‐Albornoz. The network helps show where Jorge Carrillo‐de‐Albornoz may publish in the future.
Co-authorship network of co-authors of Jorge Carrillo‐de‐Albornoz
This figure shows the co-authorship network connecting the top 25 collaborators of Jorge Carrillo‐de‐Albornoz. A scholar is included among the top collaborators of Jorge Carrillo‐de‐Albornoz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jorge Carrillo‐de‐Albornoz. Jorge Carrillo‐de‐Albornoz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | UNEDBiasTeam at IberLEF 2021's EXIST Task: Detecting Sexism Using Bias Techniques. | 1 |
| 4 | Combining Transformer-Based Models with Traditional Machine Learning Approaches for Sexism Identification in Social Networks at EXIST 2021. | 2 |
| 5 | 27 | |
| 6 | A Multi-Task and Multilingual Model for Sexism Identification in Social Networks. | 1 |
| 7 | 59 | |
| 8 | 8 | |
| 9 | 4 | |
| 10 | 40 | |
| 11 | UNED Loz Team at M-WePNaD. | 1 |
| 12 | 15 | |
| 13 | 16 | |
| 14 | 31 | |
| 15 | SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis | 72 |
| 16 | UCM-2: a Rule-Based Approach to Infer the Scope of Negation via Dependency Parsing | 5 |
| 17 | UCM-I: A Rule-based Syntactic Approach for Resolving the Scope of Negation | 12 |
| 18 | Using an Emotion-based Model and Sentiment Analysis Techniques to Classify Polarity for Reputation. | 8 |
| 19 | A Hybrid Approach to Emotional Sentence Polarity and Intensity Classification | 28 |
| 20 | 3 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.