Emanuele Bastianelli

24 papers receiving 336 citations

Peers

Emanuele Bastianelli
Comparison fields: 5 of 52
  • Artificial Intelligence 241
  • Computer Vision and Pattern Recognition 90
  • Control and Systems Engineering 68
  • Signal Processing 36
  • Social Psychology 31
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Citations per field
00.5×4.8×
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Citations per year

Countries citing papers authored by Emanuele Bastianelli

Since Specialization
Citations

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

Fields of papers citing papers by Emanuele Bastianelli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emanuele Bastianelli

This figure shows the co-authorship network connecting the top 25 collaborators of Emanuele Bastianelli. A scholar is included among the top collaborators of Emanuele Bastianelli 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 Emanuele Bastianelli. Emanuele Bastianelli 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
#WorkIndexed citations
1 7
2
Mitigating bias in deep nets with knowledge bases : The case of natural language understanding for robots
2
3 85
4 7
5
Exploring task-agnostic, ShapeNet-based object recognition for mobile robots
1
6 28
7 3
8 11
9
A discriminative approach to grounded spoken language understanding in interactive robotics
19
10
Update of time-invalid information in Knowledge Bases through Mobile Agents
1
11 4
12 28
13 14
14 2
15 10
16 36
17
HuRIC: a Human Robot Interaction Corpus
20
18
UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role Labeling
11
19
Textual Inference and Meaning Representation in Human Robot Interaction
23
20 8

About Emanuele Bastianelli

Emanuele Bastianelli is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering, having authored 24 papers that have together received 354 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (11 papers), Speech and dialogue systems (9 papers) and Topic Modeling (9 papers). The work is most often cited by research in Artificial Intelligence (241 citations), Computer Vision and Pattern Recognition (90 citations) and Signal Processing (36 citations). Emanuele Bastianelli has collaborated with scholars based in Italy, United Kingdom and Netherlands. Frequent co-authors include Daniele Nardi, Andrea Vanzo, Danilo Croce, Roberto Basili, Verena Rieser, Paweł Świętojański, Giuseppe Castellucci, Ilaria Tiddi, Luca Iocchi and Enrico Motta. Their work appears in journals such as Artificial Intelligence, The International Journal of Robotics Research and Robotics and Autonomous Systems.

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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