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.
An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools
2021382 citationsIvano Lauriola, Alberto Lavelli et al.profile →
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review
2019324 citationsAlberto Lavelli, Fabio Rinaldi et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Alberto Lavelli
Since
Specialization
Citations
This map shows the geographic impact of Alberto Lavelli'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 Alberto Lavelli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alberto Lavelli more than expected).
This network shows the impact of papers produced by Alberto Lavelli. 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 Alberto Lavelli. The network helps show where Alberto Lavelli may publish in the future.
Co-authorship network of co-authors of Alberto Lavelli
This figure shows the co-authorship network connecting the top 25 collaborators of Alberto Lavelli.
A scholar is included among the top collaborators of Alberto Lavelli 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 Alberto Lavelli. Alberto Lavelli is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Magnini, Bernardo, et al.. (2020). Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language. Language Resources and Evaluation. 2110–2119.7 indexed citations
5.
Lauriola, Ivano, et al.. (2020). Exploring the feature space of character-level embeddings.. The European Symposium on Artificial Neural Networks. 637–642.
6.
Guerini, Marco, et al.. (2013). FBK: Sentiment Analysis in Twitter with Tweetsted. Joint Conference on Lexical and Computational Semantics. 466–470.8 indexed citations
7.
Lavelli, Alberto, et al.. (2013). FBK-irst : A Multi-Phase Kernel Based Approach for Drug-Drug Interaction Detection and Classification that Exploits Linguistic Information. Joint Conference on Lexical and Computational Semantics. 351–355.67 indexed citations
8.
Lavelli, Alberto, et al.. (2012). An Evaluation of the Effect of Automatic Preprocessing on Syntactic Parsing for Biomedical Relation Extraction. Language Resources and Evaluation. 544–551.1 indexed citations
9.
Lavelli, Alberto, et al.. (2012). Impact of Less Skewed Distributions on Efficiency and Effectiveness of Biomedical Relation Extraction. International Conference on Computational Linguistics. 205–216.13 indexed citations
10.
Bongelli, Ramona, Carla Canestrari, Ilaria Riccioni, et al.. (2012). A Corpus of Scientific Biomedical Texts Spanning over 168 Years Annotated for Uncertainty. Language Resources and Evaluation. 2009–2014.7 indexed citations
11.
Lavelli, Alberto, et al.. (2011). A Study on Dependency Tree Kernels for Automatic Extraction of Protein-Protein Interaction. Institutional Research Information System (Università degli Studi di Trento). 124–133.19 indexed citations
12.
Lavelli, Alberto, et al.. (2010). Disease Mention Recognition with Specific Features. Meeting of the Association for Computational Linguistics. 83–90.29 indexed citations
13.
Bosco, Cristina, Simonetta Montemagni⋄, Alessandro Mazzei, et al.. (2010). Comparing the Influence of Different Treebank Annotations on Dependency Parsing. Language Resources and Evaluation. 1794–1801.11 indexed citations
14.
Bosco, Cristina, Alessandro Mazzei, Vincenzo Lombardo, et al.. (2008). Comparing Italian parsers on a common treebank: the Evalita experience. Language Resources and Evaluation. 2066–2073.9 indexed citations
15.
Romano, Lorenza, Milen Kouylekov, Idan Szpektor, Ido Dagan, & Alberto Lavelli. (2006). Investigating a Generic Paraphrase-Based Approach for Relation Extraction. Conference of the European Chapter of the Association for Computational Linguistics. 409–416.67 indexed citations
16.
Lavelli, Alberto, Fabrizio Sebastiani, & Roberto Zanoli. (2004). An Experimental Comparison of Term Representation for Term Management Applications.. SEBD. 190–201.1 indexed citations
17.
Iria, José, et al.. (2004). Integrating Information Extraction, Ontology Learning and Semantic Browsing into Organizational Knowledge Processes. PUB – Publications at Bielefeld University (Bielefeld University). 21(6). 11–7.3 indexed citations
18.
Lavelli, Alberto, Mary Elaine Califf, Fabio Ciravegna, et al.. (2004). A Critical Survey of the Methodology for IE Evaluation. Language Resources and Evaluation.11 indexed citations
19.
Ciravegna, Fabio, Alberto Lavelli, Nadia Mana, et al.. (1999). FACILE: classifying texts integrating pattern matching and information extraction. International Joint Conference on Artificial Intelligence. 890–895.18 indexed citations
20.
Ciravegna, Fabio & Alberto Lavelli. (1997). Controlling Bottom-Up Chart Parsers through Text Chunking.. 30–41.2 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.