Giuseppe Castellucci

858 citations
22 papers · 344 indexed · h-index 10

Giuseppe Castellucci

21 papers receiving 325 citations

Peers

Giuseppe Castellucci
Comparison fields: 5 of 64
  • Artificial Intelligence 289
  • Computer Vision and Pattern Recognition 64
  • Information Systems 58
  • General Social Sciences 7
  • Signal Processing 19
Replace Feiyu Xu with:
Feiyu Xu Germany
Anastasia Shimorina France
Avirup Sil United States
Yuanmeng Yan China
Salma Jamoussi Tunisia
Peng Qi China
Ahmed El-Kishky United States
Yee Fan Tan Singapore
Giannis Bekoulis Belgium
Giuseppe Castellucci relative to Feiyu Xu Germany Feiyu Xu's profile →
Citations per field
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Citations per year

Countries citing papers authored by Giuseppe Castellucci

Since Specialization
Citations

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

Fields of papers citing papers by Giuseppe Castellucci

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Giuseppe Castellucci. 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 Giuseppe Castellucci. The network helps show where Giuseppe Castellucci may publish in the future.

Co-authorship network

The 18 scholars most cited alongside Giuseppe Castellucci, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Giuseppe Castellucci Line = papers co-authored together Giuseppe Castellucci links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20241
2 20233
3 202115
4 20211
5 20213
6 2020108
7
KELP: a Kernel-based Learning Platform
201826
8
Transfer learning for industrial applications of named entity recognition
20183
9 201712
10 201712
11 20178
12
A language independent method for generating large scale polarity lexicons
20163
13 20152
14 201532
15
HuRIC: a Human Robot Interaction Corpus
201420
16 201421
17
UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis
20136
18
Textual Inference and Meaning Representation in Human Robot Interaction
201323
19 20130
20 20128

About Giuseppe Castellucci

Giuseppe Castellucci is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 22 papers that have together received 344 indexed citations. Recurring topics across this work include Topic Modeling (18 papers), Natural Language Processing Techniques (12 papers), Sentiment Analysis and Opinion Mining (6 papers), Multimodal Machine Learning Applications (6 papers), Text and Document Classification Technologies (4 papers), Advanced Text Analysis Techniques (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Speech and dialogue systems (2 papers). The work is most often cited by research in Artificial Intelligence (289 citations), Computer Vision and Pattern Recognition (64 citations) and Information Systems (58 citations). Giuseppe Castellucci has collaborated with scholars based in Italy, United States and Qatar. Frequent co-authors include Danilo Croce, Roberto Basili, Simone Filice, Emanuele Bastianelli, Oleg Rokhlenko, Daniele Nardi, Giovanni Da San Martino, Luca Iocchi, Heng Ji and Alessandro Moschitti. Their work appears in journals such as SHILAP Revista de lepidopterología, The International Journal of Robotics Research and Journal of Machine Learning Research.

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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