Riza Batista-Navarro

2.1k total citations · 1 hit paper
87 papers, 838 citations indexed

About

Riza Batista-Navarro is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Riza Batista-Navarro has authored 87 papers receiving a total of 838 indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Artificial Intelligence, 36 papers in Molecular Biology and 11 papers in Information Systems. Recurrent topics in Riza Batista-Navarro's work include Topic Modeling (44 papers), Natural Language Processing Techniques (36 papers) and Biomedical Text Mining and Ontologies (35 papers). Riza Batista-Navarro is often cited by papers focused on Topic Modeling (44 papers), Natural Language Processing Techniques (36 papers) and Biomedical Text Mining and Ontologies (35 papers). Riza Batista-Navarro collaborates with scholars based in United Kingdom, Philippines and Saudi Arabia. Riza Batista-Navarro's co-authors include Sophia Ananiadou, Gavin Abercrombie, Waad Alhoshan, Liping Zhao, Keletso J. Letsholo, Alessio Ferrari, Erol-Valeriu Chioasca, Rafał Rak, Paul M. Thompson and Frank Boons and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Riza Batista-Navarro

72 papers receiving 799 citations

Hit Papers

Natural Language Processing for Requirements Engineering 2021 2026 2022 2024 2021 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Natural Language Processing for Requirements Engineering
    2021 163 citations Riza Batista-Navarro et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Riza Batista-Navarro United Kingdom 16 464 260 187 51 42 87 838
Michael Elhadad Israel 21 1.9k 4.1× 245 0.9× 249 1.3× 16 0.3× 29 0.7× 73 2.3k
Ahmed Rafea Egypt 20 1.5k 3.2× 113 0.4× 389 2.1× 10 0.2× 18 0.4× 88 1.8k
Iz Beltagy United States 10 1.6k 3.4× 514 2.0× 233 1.2× 15 0.3× 81 1.9× 17 1.9k
Lovro Šubelj Slovenia 12 254 0.5× 129 0.5× 127 0.7× 12 0.2× 22 0.5× 23 646
Degen Huang China 15 798 1.7× 328 1.3× 104 0.6× 5 0.1× 59 1.4× 121 1.0k
David Pinto Mexico 18 857 1.8× 69 0.3× 365 2.0× 16 0.3× 25 0.6× 111 1.2k
Francisco García‐Sánchez Spain 19 694 1.5× 85 0.3× 663 3.5× 14 0.3× 17 0.4× 51 1.2k
Patrick Lambrix Sweden 16 653 1.4× 487 1.9× 320 1.7× 19 0.4× 41 1.0× 93 988
Carolyn E. Begg United Kingdom 9 250 0.5× 34 0.1× 395 2.1× 26 0.5× 29 0.7× 17 841
Chengjie Sun China 18 905 2.0× 349 1.3× 218 1.2× 2 0.0× 28 0.7× 71 1.2k

Countries citing papers authored by Riza Batista-Navarro

Since Specialization
Citations

This map shows the geographic impact of Riza Batista-Navarro'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 Riza Batista-Navarro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Riza Batista-Navarro more than expected).

Fields of papers citing papers by Riza Batista-Navarro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Riza Batista-Navarro. 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 Riza Batista-Navarro. The network helps show where Riza Batista-Navarro may publish in the future.

Co-authorship network of co-authors of Riza Batista-Navarro

This figure shows the co-authorship network connecting the top 25 collaborators of Riza Batista-Navarro. A scholar is included among the top collaborators of Riza Batista-Navarro 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 Riza Batista-Navarro. Riza Batista-Navarro 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.
Sun, Y. J., et al.. (2025). LVPruning: An Effective yet Simple Language-Guided Vision Token Pruning Approach for Multi-modal Large Language Models. Research Explorer (The University of Manchester). 4299–4308.
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Thompson, Paul M., et al.. (2023). Global information-aware argument mining based on a top-down multi-turn QA model. Information Processing & Management. 60(5). 103445–103445. 2 indexed citations
7.
Batista-Navarro, Riza, et al.. (2023). Argument mining as a multi-hop generative machine reading comprehension task. Research Explorer (The University of Manchester). 10846–10858.
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Batista-Navarro, Riza, et al.. (2022). Building an Ensemble of Transformer Models for Arabic Dialect Classification and Sentiment Analysis. Research Explorer (The University of Manchester). 479–484. 3 indexed citations
10.
Malik, Khaleel, et al.. (2018). Analyzing Sentiments Expressed on Twitter by UK Energy Company Consumers. Research Explorer (The University of Manchester). 95–98. 49 indexed citations
11.
Thompson, Paul M., et al.. (2018). Annotation and detection of drug effects in text for pharmacovigilance. Journal of Cheminformatics. 10(1). 37–37. 23 indexed citations
12.
Batista-Navarro, Riza, et al.. (2016). Crowdsourcing-based Annotation of Emotions in Filipino and English Tweets. International Conference on Computational Linguistics. 74–82. 5 indexed citations
13.
Batista-Navarro, Riza, Jacob Carter, & Sophia Ananiadou. (2016). Argo: enabling the development of bespoke workflows and services for disease annotation. Database. 2016. baw066–baw066. 10 indexed citations
14.
Thompson, Paul M., Riza Batista-Navarro, Georgios Kontonatsios, et al.. (2016). Text Mining the History of Medicine. PLoS ONE. 11(1). e0144717–e0144717. 37 indexed citations
15.
Fu, Xiao, Riza Batista-Navarro, Rafał Rak, & Sophia Ananiadou. (2015). Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows. Journal of Biomedical Semantics. 6(1). 8–8. 15 indexed citations
16.
Batista-Navarro, Riza, Jacob Carter, & Sophia Ananiadou. (2015). Semi-automatic curation of chronic obstructive pulmonary disease phenotypes using Argo.. Research Explorer (The University of Manchester). 406–408. 1 indexed citations
17.
Rak, Rafał, Jacob Carter, Andrew Rowley, Riza Batista-Navarro, & Sophia Ananiadou. (2014). Interoperability and Customisation of Annotation Schemata in Argo. Language Resources and Evaluation. 3837–3842. 2 indexed citations
18.
Batista-Navarro, Riza, et al.. (2012). What's in a Name? Entity Type Variation across Two Biomedical Subdomains. Conference of the European Chapter of the Association for Computational Linguistics. 38–45. 3 indexed citations
19.
Batista-Navarro, Riza & Sophia Ananiadou. (2011). Building a Coreference-Annotated Corpus from the Domain of Biochemistry. Research Explorer (The University of Manchester). 83–91. 10 indexed citations
20.
Batista-Navarro, Riza, et al.. (2010). ESP: An expert system for poisoning diagnosis and management. Informatics for Health and Social Care. 35(2). 53–63. 9 indexed citations

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.

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