Emanuele Bastianelli
- Artificial Intelligence top 5%
- Natural Language Processing Techniques 11
- Speech and dialogue systems 9
- Topic Modeling 9
- Semantic Web and Ontologies 2
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- Multimodal Machine Learning Applications 4
- Context-Aware Activity Recognition Systems 3
- Control and Systems Engineering top 10%
- Robotics and Automated Systems 6
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- Modular Robots and Swarm Intelligence 4
Emanuele Bastianelli
24 papers receiving 336 citations
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 241
- Computer Vision and Pattern Recognition 90
- Signal Processing 36
- Control and Systems Engineering 68
- Human-Computer Interaction 11
Countries citing papers authored by Emanuele Bastianelli
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
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
The 25 scholars most cited alongside Emanuele Bastianelli, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 7 | |
| 2 | Mitigating bias in deep nets with knowledge bases : The case of natural language understanding for robots | 2020 | 2 |
| 3 | 2020 | 85 | |
| 4 | 2020 | 7 | |
| 5 | Exploring task-agnostic, ShapeNet-based object recognition for mobile robots | 2019 | 1 |
| 6 | 2019 | 28 | |
| 7 | 2017 | 3 | |
| 8 | 2017 | 11 | |
| 9 | A discriminative approach to grounded spoken language understanding in interactive robotics | 2016 | 19 |
| 10 | Update of time-invalid information in Knowledge Bases through Mobile Agents | 2016 | 1 |
| 11 | 2016 | 4 | |
| 12 | 2016 | 28 | |
| 13 | 2015 | 14 | |
| 14 | 2015 | 2 | |
| 15 | 2015 | 10 | |
| 16 | 2015 | 36 | |
| 17 | HuRIC: a Human Robot Interaction Corpus | 2014 | 20 |
| 18 | UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role Labeling | 2013 | 11 |
| 19 | Textual Inference and Meaning Representation in Human Robot Interaction | 2013 | 23 |
| 20 | 2012 | 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), Topic Modeling (9 papers), Robotics and Automated Systems (6 papers), Modular Robots and Swarm Intelligence (4 papers), Multimodal Machine Learning Applications (4 papers), Context-Aware Activity Recognition Systems (3 papers) and Semantic Web and Ontologies (2 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.
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.