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.
Moses
20073.2k citationsPhilipp Koehn, Richard Zens et al.profile →
Citations per year, relative to Nicola Bertoldi Nicola Bertoldi (= 1×)
peers
Richard Zens
Countries citing papers authored by Nicola Bertoldi
Since
Specialization
Citations
This map shows the geographic impact of Nicola Bertoldi'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 Nicola Bertoldi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicola Bertoldi more than expected).
This network shows the impact of papers produced by Nicola Bertoldi. 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 Nicola Bertoldi. The network helps show where Nicola Bertoldi may publish in the future.
Co-authorship network of co-authors of Nicola Bertoldi
This figure shows the co-authorship network connecting the top 25 collaborators of Nicola Bertoldi.
A scholar is included among the top collaborators of Nicola Bertoldi 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 Nicola Bertoldi. Nicola Bertoldi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Negri, Matteo, Marco Turchi, Rajen Chatterjee, & Nicola Bertoldi. (2018). ESCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing. Language Resources and Evaluation. 24–30.32 indexed citations
4.
Cettolo, Mauro, Nicola Bertoldi, & Marcello Federico. (2014). The repetition rate of text as a predictor of the effectiveness of machine translation adaptation. Conference of the Association for Machine Translation in the Americas. 166–179.6 indexed citations
5.
Federico, Marcello, Nicola Bertoldi, Mauro Cettolo, et al.. (2014). THE MATECAT TOOL. International Conference on Computational Linguistics. 129–132.37 indexed citations
6.
Federico, Marcello & Nicola Bertoldi. (2012). Practical Domain Adaptation in SMT. Conference of the Association for Machine Translation in the Americas.1 indexed citations
7.
Bertoldi, Nicola, Mauro Cettolo, Marcello Federico, & Christian Buck. (2012). Evaluating the Learning Curve of Domain Adaptive Statistical Machine Translation Systems. Workshop on Statistical Machine Translation. 433–441.8 indexed citations
8.
Bertoldi, Nicola, P. Prinetto, Vincenzo Lombardo, et al.. (2010). On the creation and the annotation of a large-scale Italian-LIS parallel corpus. PORTO Publications Open Repository TOrino (Politecnico di Torino).10 indexed citations
Bertoldi, Nicola, Mauro Cettolo, & Marcello Federico. (2010). Statistical Machine Translation of Texts with Misspelled Words. North American Chapter of the Association for Computational Linguistics. 412–419.19 indexed citations
Cettolo, Mauro, Marcello Federico, Daniele Pighin, & Nicola Bertoldi. (2008). Shallow-Syntax Phrase-Based Translation: Joint versus Factored String-to-Chunk Models. Conference of the Association for Machine Translation in the Americas. 56–64.1 indexed citations
Chen, Boxing, Roldano Cattoni, Nicola Bertoldi, Mauro Cettolo, & Marcello Federico. (2005). The ITC-irst SMT System for IWSLT 2006. IWSLT. 53–58.11 indexed citations
20.
Bertoldi, Nicola, Roldano Cattoni, Mauro Cettolo, & Marcello Federico. (2004). The ITC-irst Statistical Machine Translation System for IWSLT-2004. IWSLT. 51–58.15 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.