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.
GENIA corpus—a semantically annotated corpus for bio-textmining
2003803 citationsJD Kim, Tomoko Ohta et al.Bioinformaticsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of J Tsujii'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 J Tsujii with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J Tsujii more than expected).
This network shows the impact of papers produced by J Tsujii. 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 J Tsujii. The network helps show where J Tsujii may publish in the future.
Co-authorship network of co-authors of J Tsujii
This figure shows the co-authorship network connecting the top 25 collaborators of J Tsujii.
A scholar is included among the top collaborators of J Tsujii 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 J Tsujii. J Tsujii 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.
Kontonatsios, Georgios, et al.. (2013). Using Random Forest to recognise translation equivalents of biomedical terms across languages. Research Explorer (The University of Manchester). 95–104.1 indexed citations
2.
Kim, JD, et al.. (2012). Selected articles from the BioNLP Shared Task 2011. BMC Bioinformatics.1 indexed citations
3.
Ohta, Tomoko, Sampo Pyysalo, Sophia Ananiadou, & J Tsujii. (2011). Pathway Curation Support as an Information Extraction Task. Research Explorer (The University of Manchester).6 indexed citations
4.
Stenetorp, Pontus, Sampo Pyysalo, Sophia Ananiadou, & J Tsujii. (2011). Almost Total Recall: Semantic Category Disambiguation Using Large Lexical Resources and Approximate String Matching. Research Explorer (The University of Manchester).2 indexed citations
Nobata, Chikashi, et al.. (2009). Semantic Search on Digital Document Repositories based on Text Mining Results. Research Explorer (The University of Manchester). 34–48.9 indexed citations
Tsujii, J, et al.. (2001). [Natural language processing for text mining in genome science].. PubMed. 46(16 Suppl). 2532–7.1 indexed citations
12.
Mima, Hideki, Sophia Ananiadou, & J Tsujii. (1999). A Web-based integrated knowledge mining aid system using term-oriented Natural Language Processing. Research Explorer (The University of Manchester). 230(4). 13–18.4 indexed citations
13.
Frantzi, Katerina T., et al.. (1999). Automatic classification of technical terms using the NC-value method for term recognition. Research Explorer (The University of Manchester). 57–66.2 indexed citations
14.
Hishiki, Teruyoshi, et al.. (1998). Developing NLP Tools for Genome Informatics: An Information Extraction Perspective.. PubMed. 9. 81–90.14 indexed citations
15.
Ananiadou, Sophia & J Tsujii. (1997). Term disambiguation by adding structural constraints to lexically-based context matching techniques.1 indexed citations
16.
Frantzi, Katerina T., et al.. (1997). Term identification using contextual cues. Research Explorer (The University of Manchester).
17.
Frantzi, Katerina T., et al.. (1996). Extracting terminological expressions. Research Explorer (The University of Manchester).4 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.