This map shows the geographic impact of Pablo Gervás'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 Pablo Gervás with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pablo Gervás more than expected).
This network shows the impact of papers produced by Pablo Gervás. 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 Pablo Gervás. The network helps show where Pablo Gervás may publish in the future.
Co-authorship network of co-authors of Pablo Gervás
This figure shows the co-authorship network connecting the top 25 collaborators of Pablo Gervás.
A scholar is included among the top collaborators of Pablo Gervás 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 Pablo Gervás. Pablo Gervás is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Gervás, Pablo, et al.. (2018). INES: A reconstruction of the Charade storytelling system using the Afanasyev Framework.. ICCC. 48–54.3 indexed citations
2.
Hervás, Raquel, et al.. (2016). Exploring the Role of Word Associations in the Construction of Rhetorical Figures.. ICCC. 222–229.1 indexed citations
3.
Gervás, Pablo & Carlos León. (2015). When reflective feedback triggers goal revision: a computational model for literary creativity. International Joint Conference on Artificial Intelligence. 32–39.1 indexed citations
4.
León, Carlos & Pablo Gervás. (2014). Creativity in Story Generation From the Ground Up: Non-deterministic Simulation driven by Narrative.. ICCC. 201–210.12 indexed citations
5.
Finlayson, Mark A., et al.. (2014). Computational Models of Narrative: Using Artificial Intelligence to Operationalize Russian Formalist and French Structuralist Theories.. DH.1 indexed citations
6.
Oliveira, Hugo Gonçalo, Raquel Hervás, Alberto Díaz, & Pablo Gervás. (2014). Adapting a Generic Platform for Poetry Generation to Produce Spanish Poems.. ICCC. 63–71.12 indexed citations
7.
Gervás, Pablo & Carlos León. (2014). Reading and Writing as a Creative Cycle: the Need for a Computational Model.. ICCC. 182–191.6 indexed citations
8.
Ballesteros, Miguel, et al.. (2012). Analyzing the CoNLL--X Shared Task from a Sentence Accuracy Perspective. Procesamiento del lenguaje natural. 48(48). 29–34.1 indexed citations
9.
Ballesteros, Miguel, et al.. (2012). UCM-2: a Rule-Based Approach to Infer the Scope of Negation via Dependency Parsing. Joint Conference on Lexical and Computational Semantics. 1. 288–293.5 indexed citations
10.
Carrillo‐de‐Albornoz, Jorge, Laura Plaza, & Pablo Gervás. (2012). SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis. Language Resources and Evaluation. 3562–3567.72 indexed citations
Carrillo‐de‐Albornoz, Jorge, Laura Plaza, & Pablo Gervás. (2010). A Hybrid Approach to Emotional Sentence Polarity and Intensity Classification. 153–161.28 indexed citations
16.
Gervás, Pablo & Carlos León. (2010). Story Generation Driven by System-Modified Evaluation Validated by Human Judges.. ICCC. 85–89.1 indexed citations
17.
Hervás, Raquel & Pablo Gervás. (2008). Descripción de entidades y generación de expresiones de referencia en la generación automática de discurso. Procesamiento del lenguaje natural. 41(41). 217–224.1 indexed citations
Gervás, Pablo, et al.. (2006). Análisis de dependencias para la marcación de cuentos con emociones. Procesamiento del lenguaje natural. 37(37). 137–144.
20.
Hervás, Raquel & Pablo Gervás. (2005). An Evolutionary Approach to Referring Expression Generation and Aggregation.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
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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.