Demo Data from EDBT 2026 / reviewers in the wild / expert
Zhuoming Zheng
dblp:324/0905
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0002-6506-2945ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › sentiment analysis › aspect-based sentiment analysis
aspect extraction |
0.6 | 1 | 2022 | Enhance Weakly-Supervised Aspect Detection with External Knowledge (Student Abstract) · AAAI 2022 |
Natural language and speech › Information extraction and text analysis › sentiment analysis › aspect-based sentiment analysis › aspect extraction
weakly-supervised aspect detection |
0.6 | 1 | 2022 | Enhance Weakly-Supervised Aspect Detection with External Knowledge (Student Abstract) · AAAI 2022 |
Methods — techniques the papers use, named apart from their topics
external knowledge incorporation · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Grouped top-down reasoning with hierarchical window transformer for visual grounding
Liuwu Li, Zhuoming Zheng, Yuqi Bu, Cantao Wu, Shubin Huang, Qingbao Huang, Yi Cai 0001 |
Inf. Process. Manag. | 2 |
| 2022 | Enhance Weakly-Supervised Aspect Detection with External Knowledge (Student Abstract)abstractAspect detection aims to identify aspects of reviews and is an essential up-stream task of opinion mining and so on. However, existing weakly-supervised methods suffer from lacking the ability of identifying implicit aspects with infrequent aspect terms and "Misc" aspects. To tackle these problems, we propose to enhance the representation of segment with external knowledge by a weakly-supervised method. Experiments demonstrate the effectiveness of our model and the improvement by incorporating external knowledge. Zhuoming Zheng, Liuwu Li |
AAAI | 1 |