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.
Deep learning with word embeddings improves biomedical named entity recognition
2017356 citationsMaryam Habibi, Leon Weber et al.Bioinformaticsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Mariana Neves'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 Mariana Neves with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mariana Neves more than expected).
This network shows the impact of papers produced by Mariana Neves. 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 Mariana Neves. The network helps show where Mariana Neves may publish in the future.
Co-authorship network of co-authors of Mariana Neves
This figure shows the co-authorship network connecting the top 25 collaborators of Mariana Neves.
A scholar is included among the top collaborators of Mariana Neves 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 Mariana Neves. Mariana Neves is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Neves, Mariana, et al.. (2019). Overview of the CLEF eHealth 2019 Multilingual Information Extraction.. CLEF (Working Notes).9 indexed citations
5.
Neves, Mariana, Antonio Jimeno Yepes, Aurélie Névéol, et al.. (2018). Findings of the WMT 2018 Biomedical Translation Shared Task: Evaluation on Medline test sets. HAL (Le Centre pour la Communication Scientifique Directe).
6.
Habibi, Maryam, Leon Weber, Mariana Neves, David Luis Wiegandt, & Ulf Leser. (2017). Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics. 33(14). i37–i48.356 indexed citations breakdown →
Neves, Mariana, Antonio Jimeno Yepes, & Aurélie Névéol. (2016). The Scielo Corpus: a Parallel Corpus of Scientific Publications for Biomedicine.. Language Resources and Evaluation. 2942–2948.19 indexed citations
10.
Neves, Mariana, et al.. (2016). Entity-Supported Summarization of Biomedical Abstracts.. International Conference on Computational Linguistics. 40–49.11 indexed citations
11.
Neves, Mariana. (2015). HPI question answering system in the BioASQ 2015 challenge. CLEF (Working Notes).7 indexed citations
12.
Herbst, Konrad, et al.. (2014). Applying In-Memory Technology for Automatic Template Filling in the Clinical Domain.. CLEF (Working Notes). 91–102.2 indexed citations
13.
Neves, Mariana. (2014). HPI in-memory-based Database System in Task 2b of BioASQ.. CLEF (Working Notes). 1337–1347.4 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.