Riza Batista-Navarro
- Artificial Intelligence top 5%
- Molecular Biology
- Information Systems top 5%
- Software top 10%
- Computational Theory and Mathematics top 10%
- Co-authors
- Sophia AnaniadouGavin AbercrombieLiping ZhaoWaad AlhoshanKeletso J. LetsholoErol-Valeriu ChioascaAlessio FerrariPaul M. Thompson
- Topics
- Topic Modeling (44 papers)Natural Language Processing Techniques (36 papers)Biomedical Text Mining and Ontologies (35 papers)
- Journals
- SHILAP Revista de lepidopterologíaBioinformaticsPLoS ONE
- Partner nations
- United KingdomPhilippinesSaudi Arabia
In The Last Decade
Riza Batista-Navarro
72 papers receiving 799 citations
Hit Papers
Peers
Comparison fields: 5 of 116
- Artificial Intelligence 464
- Molecular Biology 260
- Information Systems 187
- Software 51
- Computational Theory and Mathematics 42
Countries citing papers authored by Riza Batista-Navarro
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 3 | |
| 7 | 0 | |
| 8 | 0 | |
| 9 | 1 | |
| 10 | 7 | |
| 11 | 3 | |
| 12 | 1 | |
| 13 | 3 | |
| 14 | Crowdsourcing-based Annotation of Emotions in Filipino and English Tweets | 5 |
| 15 | Interoperability and Customisation of Annotation Schemata in Argo | 2 |
| 16 | 8 | |
| 17 | What's in a Name? Entity Type Variation across Two Biomedical Subdomains | 3 |
| 18 | Building a Coreference-Annotated Corpus from the Domain of Biochemistry | 10 |
| 19 | 28 | |
| 20 | 9 |
About Riza Batista-Navarro
Riza Batista-Navarro is a scholar working on Artificial Intelligence, Ecological Modeling and General Social Sciences, having authored 87 papers that have together received 838 indexed citations. Recurring topics across this work include Topic Modeling (44 papers), Natural Language Processing Techniques (36 papers) and Biomedical Text Mining and Ontologies (35 papers). The work is most often cited by research in Artificial Intelligence (464 citations), Software (51 citations) and General Social Sciences (35 citations). Riza Batista-Navarro has collaborated with scholars based in United Kingdom, Philippines and Saudi Arabia. Frequent co-authors include Sophia Ananiadou, Gavin Abercrombie, Liping Zhao, Waad Alhoshan, Keletso J. Letsholo, Erol-Valeriu Chioasca, Alessio Ferrari, Paul M. Thompson, Rafał Rak and Frank Boons. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.
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.