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.Neurocomputingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Ivano Lauriola
Since
Specialization
Citations
This map shows the geographic impact of Ivano Lauriola'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 Ivano Lauriola with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivano Lauriola more than expected).
This network shows the impact of papers produced by Ivano Lauriola. 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 Ivano Lauriola. The network helps show where Ivano Lauriola may publish in the future.
Co-authorship network of co-authors of Ivano Lauriola
This figure shows the co-authorship network connecting the top 25 collaborators of Ivano Lauriola.
A scholar is included among the top collaborators of Ivano Lauriola 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 Ivano Lauriola. Ivano Lauriola is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lauriola, Ivano, Alberto Lavelli, & Fabio Aiolli. (2021). An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools. Neurocomputing. 470. 443–456.382 indexed citations breakdown →
8.
Lauriola, Ivano, et al.. (2020). Automatic Detection of Cross-language Verbal Deception. eScholarship (California Digital Library). 1756–1762.2 indexed citations
9.
Lauriola, Ivano, et al.. (2020). Exploring the feature space of character-level embeddings.. The European Symposium on Artificial Neural Networks. 637–642.
10.
Lauriola, Ivano, et al.. (2020). DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text.. Language Resources and Evaluation. 1423–1430.9 indexed citations
Lauriola, Ivano, Michele Donini, & Fabio Aiolli. (2017). Learning dot-product polynomials for multiclass problems.. The European Symposium on Artificial Neural Networks.2 indexed citations
17.
Donini, Michele, Nicolò Navarin, Ivano Lauriola, Fabio Aiolli, & Fabrizio Costa. (2017). Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning.. Research Padua Archive (University of Padua).4 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.