Giuseppe Carenini

167 papers receiving 3.3k citations

Hit Papers

Tracking COVID-19 Discourse on Twitter in North America: ...20212026202220242021255075100

Peers

Giuseppe Carenini
Comparison fields: 5 of 119
  • Artificial Intelligence 2.5k
  • Computer Vision and Pattern Recognition 867
  • Information Systems 507
  • Sociology and Political Science 375
  • Human-Computer Interaction 327
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Citations per year

Countries citing papers authored by Giuseppe Carenini

Since Specialization
Citations

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

Fields of papers citing papers by Giuseppe Carenini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Giuseppe Carenini

This figure shows the co-authorship network connecting the top 25 collaborators of Giuseppe Carenini. A scholar is included among the top collaborators of Giuseppe Carenini 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 Giuseppe Carenini. Giuseppe Carenini 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 1
2 0
3 2
4 14
5 22
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Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysisbreakdown →
109
7 7
8 5
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Chat Disentanglement: Identifying Semantic Reply Relationships with Random Forests and Recurrent Neural Networks
17
10
Predicting confusion in information visualization from eye tracking and interaction data
33
11
Training Data Enrichment for Infrequent Discourse Relations
5
12 61
13 0
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Abstractive Meeting Summarization with Entailment and Fusion
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Combining Intra- and Multi-sentential Rhetorical Parsing for Document-level Discourse Analysis
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A Novel Discriminative Framework for Sentence-Level Discourse Analysis
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User-adaptive visualizations: can gaze data tell us when a user needs them?
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Exploring more realistic evaluation measures for collaborative filtering
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Proceedings of the Seventh International Conference on User Modeling
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A Principled Representation of Attributive Descriptions for Generating Integrated Text and Information Graphics Presentations
7

About Giuseppe Carenini

Giuseppe Carenini is a scholar working on Artificial Intelligence, Human-Computer Interaction and Computer Vision and Pattern Recognition, having authored 174 papers that have together received 3.6k indexed citations. Recurring topics across this work include Topic Modeling (68 papers), Natural Language Processing Techniques (53 papers) and Advanced Text Analysis Techniques (46 papers). The work is most often cited by research in Artificial Intelligence (2.5k citations), Human-Computer Interaction (327 citations) and Computer Vision and Pattern Recognition (867 citations). Giuseppe Carenini has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Raymond T. Ng, Johanna D. Moore, Cristina Conati, Ben Steichen, Gabriel Murray, Enamul Hoque, Shafiq Joty, Yashar Mehdad, Dereck Toker and Wen Xiao. Their work appears in journals such as Artificial Intelligence, Journal of Medical Internet Research and IEEE Transactions on Knowledge and Data Engineering.

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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