Countries citing papers authored by Tapio Salakoski
Since
Specialization
Citations
This map shows the geographic impact of Tapio Salakoski'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 Tapio Salakoski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tapio Salakoski more than expected).
This network shows the impact of papers produced by Tapio Salakoski. 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 Tapio Salakoski. The network helps show where Tapio Salakoski may publish in the future.
Co-authorship network of co-authors of Tapio Salakoski
This figure shows the co-authorship network connecting the top 25 collaborators of Tapio Salakoski.
A scholar is included among the top collaborators of Tapio Salakoski 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 Tapio Salakoski. Tapio Salakoski is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Mehryary, Farrokh, Hans Moen, Tapio Salakoski, & Filip Ginter. (2020). Entity-Pair Embeddings for Improving Relation Extraction in the Biomedical Domain.. The European Symposium on Artificial Neural Networks. 613–618.1 indexed citations
Björne, Jari & Tapio Salakoski. (2013). TEES 2.1: Automated Annotation Scheme Learning in the BioNLP 2013 Shared Task. Meeting of the Association for Computational Linguistics. 16–25.75 indexed citations
9.
Björne, Jari, Sofie Van Landeghem, Sampo Pyysalo, et al.. (2012). PubMed-Scale Event Extraction for Post-Translational Modifications, Epigenetics and Protein Structural Relations. Research Explorer (The University of Manchester). 82–90.9 indexed citations
10.
Stock, Michiel, Tapio Pahikkala, Antti Airola, et al.. (2012). Learning monadic and dyadic relations : three case studies in systems biology. Ghent University Academic Bibliography (Ghent University). 74–84.1 indexed citations
11.
Ananiadou, Sophia, Sampo Pyysalo, Dietrich Rebholz‐Schuhmann, Fabio Rinaldi, & Tapio Salakoski. (2012). Proceedings of the 5th International Symposium on Semantic Mining in Biomedicine (SMBM 2012).2 indexed citations
12.
Björne, Jari & Tapio Salakoski. (2011). Generalizing Biomedical Event Extraction. Meeting of the Association for Computational Linguistics. 183–191.98 indexed citations
Pahikkala, Tapio, Antti Airola, Hanna Suominen, Jorma Boberg, & Tapio Salakoski. (2008). Efficient AUC Maximization with Regularized Least-Squares. 12–19.7 indexed citations
17.
Back, Ralph‐Johan, et al.. (2006). Why complicate things?: introducing programming in high school using Python. Australasian Computing Education Conference. 71–80.72 indexed citations
18.
Salakoski, Tapio, et al.. (2006). Advances in natural language processing : 5th International Conference on NLP, FinTAL 2006, Turku, Finland, August 23-25, 2006 : proceedings. Springer eBooks.2 indexed citations
19.
Pahikkala, Tapio, Sampo Pyysalo, Filip Ginter, et al.. (2005). Kernels Incorporating Word Positional Information in Natural Language Disambiguation Tasks.. The Florida AI Research Society. 442–448.9 indexed citations
20.
Ginter, Filip, Jorma Boberg, Jouni Järvinen, & Tapio Salakoski. (2004). New Techniques for Disambiguation in Natural Language and Their Application to Biological Text. Journal of Machine Learning Research. 5. 605–621.34 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.