Fabio Massimo Zanzotto

2.8k total citations
99 papers, 1.4k citations indexed

About

Fabio Massimo Zanzotto is a scholar working on Artificial Intelligence, Information Systems and Molecular Biology. According to data from OpenAlex, Fabio Massimo Zanzotto has authored 99 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Artificial Intelligence, 12 papers in Information Systems and 6 papers in Molecular Biology. Recurrent topics in Fabio Massimo Zanzotto's work include Natural Language Processing Techniques (61 papers), Topic Modeling (57 papers) and Semantic Web and Ontologies (15 papers). Fabio Massimo Zanzotto is often cited by papers focused on Natural Language Processing Techniques (61 papers), Topic Modeling (57 papers) and Semantic Web and Ontologies (15 papers). Fabio Massimo Zanzotto collaborates with scholars based in Italy, United Kingdom and United States. Fabio Massimo Zanzotto's co-authors include Alessandro Moschitti, Marco Pennacchiotti, Patrizia Ferroni, Ido Dagan, Dan Roth, Mark Sammons, Noemi Scarpato, Silvia Riondino, Mario Roselli and Francesca Fallucchi and has published in prestigious journals such as Cancer Research, International Journal of Molecular Sciences and Annals of Oncology.

In The Last Decade

Fabio Massimo Zanzotto

89 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fabio Massimo Zanzotto Italy 21 1.1k 131 119 104 100 99 1.4k
Noemi Scarpato Italy 11 165 0.2× 171 1.3× 56 0.5× 70 0.7× 96 1.0× 25 610
André Freitas United Kingdom 15 583 0.5× 110 0.8× 103 0.9× 4 0.0× 28 0.3× 104 925
Eric W. Brown United States 14 1.2k 1.1× 397 3.0× 144 1.2× 3 0.0× 167 1.7× 35 1.9k
R. Bharat Rao Germany 14 505 0.5× 87 0.7× 94 0.8× 3 0.0× 74 0.7× 36 882
Renata Vieira Brazil 21 999 0.9× 177 1.4× 127 1.1× 2 0.0× 169 1.7× 165 1.7k
Oya Beyan Germany 17 406 0.4× 118 0.9× 203 1.7× 3 0.0× 227 2.3× 64 999
Nikita Jain India 17 424 0.4× 86 0.7× 161 1.4× 2 0.0× 172 1.7× 85 1.3k
裕二 池谷 United States 10 948 0.9× 46 0.4× 534 4.5× 2 0.0× 122 1.2× 19 1.3k
Sarthak Pati United States 11 585 0.5× 77 0.6× 42 0.4× 2 0.0× 443 4.4× 27 1.1k
Stefan Schulz Germany 22 1.5k 1.4× 181 1.4× 1.5k 12.5× 3 0.0× 61 0.6× 187 2.3k

Countries citing papers authored by Fabio Massimo Zanzotto

Since Specialization
Citations

This map shows the geographic impact of Fabio Massimo Zanzotto'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 Fabio Massimo Zanzotto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabio Massimo Zanzotto more than expected).

Fields of papers citing papers by Fabio Massimo Zanzotto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fabio Massimo Zanzotto. 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 Fabio Massimo Zanzotto. The network helps show where Fabio Massimo Zanzotto may publish in the future.

Co-authorship network of co-authors of Fabio Massimo Zanzotto

This figure shows the co-authorship network connecting the top 25 collaborators of Fabio Massimo Zanzotto. A scholar is included among the top collaborators of Fabio Massimo Zanzotto 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 Fabio Massimo Zanzotto. Fabio Massimo Zanzotto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Favalli, Andrea, et al.. (2024). Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL translation. Cineca Institutional Research Information System (Tor Vergata University). 13909–13920. 1 indexed citations
2.
Pucci, Giulia, et al.. (2024). A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages. 1229–1241.
3.
Bedran, Georges, Cátia Pesquita, Daniel Faria, et al.. (2023). Abstract 6577: CARMEN: A pan-HLA and pan-cancer proteogenomic database on antigen presentation to support cancer immunotherapy. Cancer Research. 83(7_Supplement). 6577–6577. 1 indexed citations
4.
Zanzotto, Fabio Massimo, et al.. (2023). An Approach for Awareness and Assessment of Risks in Outdoor Sports Activities. 1522–1529.
5.
Fallucchi, Francesca, et al.. (2022). Syntax and prejudice: ethically-charged biases of a syntax-based hate speech recognizer unveiled. PeerJ Computer Science. 8. e859–e859. 7 indexed citations
6.
Fallucchi, Francesca, et al.. (2021). Dis-Cover AI Minds to Preserve Human Knowledge. Future Internet. 14(1). 10–10. 14 indexed citations
7.
Fallucchi, Francesca, et al.. (2020). Pat-in-the-Loop: Declarative Knowledge for Controlling Neural Networks. Future Internet. 12(12). 218–218. 3 indexed citations
8.
Ferroni, Patrizia, Fabio Massimo Zanzotto, Silvia Riondino, et al.. (2019). Breast Cancer Prognosis Using a Machine Learning Approach. Cancers. 11(3). 328–328. 121 indexed citations
9.
Zanzotto, Fabio Massimo, et al.. (2014). Towards Syntax-aware Compositional Distributional Semantic Models. Cineca Institutional Research Information System (Tor Vergata University). 721–730. 7 indexed citations
10.
Vetere, Guido, et al.. (2011). Senso Comune, an Open Knowledge Base of Italian Language. 52. 217–243. 1 indexed citations
11.
Zanzotto, Fabio Massimo, et al.. (2011). Linguistic Redundancy in Twitter. Cineca Institutional Research Information System (Tor Vergata University). 659–669. 38 indexed citations
12.
Zanzotto, Fabio Massimo, Ioannis Korkontzelos, Francesca Fallucchi, & Suresh Manandhar. (2010). Estimating Linear Models for Compositional Distributional Semantics. Research Explorer (The University of Manchester). 1263–1271. 85 indexed citations
13.
Mehdad, Yashar, Alessandro Moschitti, & Fabio Massimo Zanzotto. (2009). SemKer: Syntactic/Semantic Kernels for Recognizing Textual Entailment.. Theory and applications of categories. 5 indexed citations
14.
Pazienza, Maria Teresa, Marco Pennacchiotti, & Fabio Massimo Zanzotto. (2006). Mixing WordNet, VerbNet and PropBank for studying verb relations. Language Resources and Evaluation. 1372–1377. 7 indexed citations
15.
Basili, Roberto, et al.. (2005). Ontology-driven Information Retrieval in FF-Poirot.. 1 indexed citations
16.
Guthrie, Louise, Roberto Basili, Fabio Massimo Zanzotto, et al.. (2004). Large Scale Experiments for Semantic Labeling of Noun Phrases in Raw Text.. Language Resources and Evaluation.
17.
Basili, Roberto, Maria Teresa Pazienza, & Fabio Massimo Zanzotto. (2002). Acquisition of domain conceptual dictionaries via decision tree learning. European Conference on Artificial Intelligence. 480–484. 1 indexed citations
18.
Basili, Roberto & Fabio Massimo Zanzotto. (2002). Parsing engineering and empirical robustness. Natural Language Engineering. 8(2-3). 97–120. 35 indexed citations
19.
Basili, Roberto, et al.. (1999). Adaptive parsing for time-constrained tasks. Cineca Institutional Research Information System (Tor Vergata University). 1 indexed citations
20.
Basili, Roberto, Maria Teresa Pazienza, & Fabio Massimo Zanzotto. (1998). Efficient Parsing for Information Extraction.. Cineca Institutional Research Information System (Tor Vergata University). 135–139. 12 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.

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