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.
chrF: character n-gram F-score for automatic MT evaluation
This map shows the geographic impact of Maja Popović'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 Maja Popović with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maja Popović more than expected).
This network shows the impact of papers produced by Maja Popović. 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 Maja Popović. The network helps show where Maja Popović may publish in the future.
Co-authorship network of co-authors of Maja Popović
This figure shows the co-authorship network connecting the top 25 collaborators of Maja Popović.
A scholar is included among the top collaborators of Maja Popović 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 Maja Popović. Maja Popović is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Castilho, Sheila, Maja Popović, & Andy Way. (2020). On Context Span Needed for Machine Translation Evaluation.. Language Resources and Evaluation. 3735–3742.8 indexed citations
6.
Popović, Maja, Alberto Poncelas, Marija Brkić Bakarić, & Andy Way. (2020). Neural Machine Translation for translating into Croatian and Serbian. Dublin City University Open Access Institutional Repository (Dublin City University). 102–113.6 indexed citations
7.
Popović, Maja, et al.. (2019). A systematic comparison between SMT and NMT on translating user-generated content. Dublin City University Open Access Institutional Repository (Dublin City University).4 indexed citations
8.
Avramidis, Eleftherios, et al.. (2016). Tools and Guidelines for Principled Machine Translation Development. Language Resources and Evaluation. 1877–1882.4 indexed citations
9.
Popović, Maja. (2015). chrF: character n-gram F-score for automatic MT evaluation. 392–395.538 indexed citations breakdown →
10.
Avramidis, Eleftherios, et al.. (2014). The taraX"U corpus of human-annotated machine translations. Language Resources and Evaluation. 2679–2682.3 indexed citations
11.
Avramidis, Eleftherios & Maja Popović. (2014). Correlating decoding events with errors in Statistical Machine Translation. 20–29.1 indexed citations
12.
Avramidis, Eleftherios & Maja Popović. (2013). Selecting Feature Sets for Comparative and Time-Oriented Quality Estimation of Machine Translation Output. Workshop on Statistical Machine Translation. 329–336.2 indexed citations
13.
Popović, Maja. (2012). Morpheme- and POS-based IBM1 and language model scores for translation quality estimation. Workshop on Statistical Machine Translation. 133–137.8 indexed citations
14.
Popović, Maja. (2012). Class error rates for evaluation of machine translation output. Workshop on Statistical Machine Translation. 71–75.8 indexed citations
15.
Vilar, David, et al.. (2011). DFKI's SC and MT Submissions to IWSLT 2011. IWSLT. 98–105.4 indexed citations
16.
Popović, Maja, David Vilar, Eleftherios Avramidis, & Aljoscha Burchardt. (2011). Evaluation without references: IBM1 scores as evaluation metrics. Workshop on Statistical Machine Translation. 99–103.13 indexed citations
Vilar, David, Maja Popović, & Hermann Ney. (2006). AER: do we need to "improve" our alignments?. IWSLT. 205–212.37 indexed citations
19.
Popović, Maja, et al.. (2005). Augmenting a Small Parallel Text with Morpho-Syntactic Language. RWTH Publications (RWTH Aachen). 41–48.2 indexed citations
20.
Ney, Hermann, et al.. (2004). Error Measures and Bayes Decision Rules Revisited with Applications to POS Tagging. RWTH Publications (RWTH Aachen). 270–276.1 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.