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.
Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors
2014761 citationsMarco Baroni, Georgiana Dinu et al.INFM-OAR (INFN Catania)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Georgiana Dinu
Since
Specialization
Citations
This map shows the geographic impact of Georgiana Dinu'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 Georgiana Dinu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Georgiana Dinu more than expected).
This network shows the impact of papers produced by Georgiana Dinu. 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 Georgiana Dinu. The network helps show where Georgiana Dinu may publish in the future.
Co-authorship network of co-authors of Georgiana Dinu
This figure shows the co-authorship network connecting the top 25 collaborators of Georgiana Dinu.
A scholar is included among the top collaborators of Georgiana Dinu 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 Georgiana Dinu. Georgiana Dinu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dinu, Georgiana, et al.. (2020). How Should Markup Tags Be Translated?. Empirical Methods in Natural Language Processing. 1160–1173.2 indexed citations
6.
Sil, Avirup, Georgiana Dinu, Gourab Kundu, & Radu Florian. (2017). The IBM Systems for Entity Discovery and Linking at TAC 2017.. Theory and applications of categories.1 indexed citations
7.
Sil, Avirup, Georgiana Dinu, & Radu Florian. (2015). The IBM Systems for Trilingual Entity Discovery and Linking at TAC 2015.. Theory and applications of categories.10 indexed citations
Bernardi, Raffaella, Georgiana Dinu, Marco Marelli, & Marco Baroni. (2013). A relatedness benchmark to test the role of determiners in compositional distributional semantics. BOA (University of Milano-Bicocca). 2. 53–57.14 indexed citations
12.
Dinu, Georgiana & Marco Baroni. (2013). General estimation and evaluation of compositional distributional semantic models. Institutional Research Information System (Università degli Studi di Trento). 50–58.30 indexed citations
13.
Dinu, Georgiana & Marco Baroni. (2013). DISSECT - DIStributional SEmantics Composition Toolkit. Institutional Research Information System (Università degli Studi di Trento). 31–36.55 indexed citations
14.
Dinu, Georgiana, et al.. (2012). A comparison of models of word meaning in context. North American Chapter of the Association for Computational Linguistics. 611–615.9 indexed citations
15.
Dinu, Georgiana & Stefan Thater. (2012). Saarland: Vector-based models of semantic textual similarity. Joint Conference on Lexical and Computational Semantics. 603–607.4 indexed citations
16.
Dinu, Georgiana & Mirella Lapata. (2010). COLING 2010, 23rd International Conference on Computational Linguistics, Posters Volume, 23-27 August 2010, Beijing, China.2 indexed citations
17.
Dinu, Georgiana & Mirella Lapata. (2010). Measuring Distributional Similarity in Context. Edinburgh Research Explorer (University of Edinburgh). 1162–1172.73 indexed citations
18.
Dinu, Georgiana & Mirella Lapata. (2010). Topic Models for Meaning Similarity in Context. Edinburgh Research Explorer (University of Edinburgh). 250–258.13 indexed citations
19.
Chrupała, Grzegorz, Georgiana Dinu, & Josef van Genabith. (2008). Learning Morphology with Morfette. Language Resources and Evaluation.66 indexed citations
20.
Chrupała, Grzegorz, et al.. (2007). Better training for function labeling. Arrow@dit (Dublin Institute of Technology).10 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.