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.
Countries citing papers authored by Roberto Navigli
Since
Specialization
Citations
This map shows the geographic impact of Roberto Navigli'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 Roberto Navigli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roberto Navigli more than expected).
This network shows the impact of papers produced by Roberto Navigli. 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 Roberto Navigli. The network helps show where Roberto Navigli may publish in the future.
Co-authorship network of co-authors of Roberto Navigli
This figure shows the co-authorship network connecting the top 25 collaborators of Roberto Navigli.
A scholar is included among the top collaborators of Roberto Navigli 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 Roberto Navigli. Roberto Navigli is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Navigli, Roberto, et al.. (2022). ExtEnD: Extractive Entity Disambiguation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2478–2488.22 indexed citations
Di, Luigi, et al.. (2020). Building Semantic Grams of Human Knowledge. Language Resources and Evaluation. 2991–3000.1 indexed citations
11.
Pasini, Tommaso, et al.. (2020). Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains. Language Resources and Evaluation. 5905–5911.6 indexed citations
Pilehvar, Mohammad Taher & Roberto Navigli. (2013). Paving the Way to a Large-scale Pseudosense-annotated Dataset. IRIS Research product catalog (Sapienza University of Rome). 1100–1109.5 indexed citations
14.
Navigli, Roberto & Daniele Vannella. (2013). SemEval-2013 Task 11: Word Sense Induction and Disambiguation within an End-User Application. Joint Conference on Lexical and Computational Semantics. 2. 193–201.29 indexed citations
Ponzetto, Simone Paolo & Roberto Navigli. (2010). Knowledge-Rich Word Sense Disambiguation Rivaling Supervised Systems. MADOC (University of Mannheim). 1522–1531.133 indexed citations
17.
Navigli, Roberto & Paola Velardi. (2006). Enriching a Formal Ontology with a Thesaurus: an Application in the Cultural Heritage Domain. Meeting of the Association for Computational Linguistics. 1–9.18 indexed citations
Cucchiarelli, Alessandro, et al.. (2004). Automatic Generation of Glosses in the OntoLearn System. IRIS Research product catalog (Sapienza University of Rome).1 indexed citations
20.
Navigli, Roberto & Paola Velardi. (2004). Structural semantic interconnection: a knowledge-based approach to Word Sense Disambiguation. Meeting of the Association for Computational Linguistics. 179–182.7 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.