Demo Data from VLDB 2026 / reviewers in the wild / expert
Renaud Lecoeuche
dblp:46/6980
· DBLP profile ↗
4ranked-venue papers
4as first author
0since 2021 · last 2001
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-authorSoftware engineering, systems software and programming languages · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author
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.
| Software engineering, system software, and programming languages
2 papers |
Requirements engineering and software design · 100% | |
| Artificial intelligence
1 paper |
Question answering and dialogue systems · 77% Knowledge representation and reasoning · 23% | |
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Question answering and dialogue systems
dialogue management |
0.0 | 1 | 2000 | Evaluating focus theories for dialogue management · Int. J. Hum. Comput. Stud. 2000 |
Requirements engineering and software design
requirements analysis |
0.0 | 1 | 2000 | Finding Comparatively Important Concepts between Texts · ASE 2000 |
Requirements engineering and software design › requirements analysis
requirements classification |
0.0 | 1 | 2000 | Finding Comparatively Important Concepts between Texts · ASE 2000 |
Requirements engineering and software design
requirements elicitation |
0.0 | 1 | 1999 | Using Focus Rules in Requirements Elicitation Dialogues · IJCAI 1999 |
Information retrieval
text analysis |
0.0 | 1 | 2000 | Finding Comparatively Important Concepts between Texts · ASE 2000 |
Methods — techniques the papers use, named apart from their topics
frequency-based concept identification · 0.1comparative text analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Learning Optimal Dialogue Management Rules by Using Reinforcement Learning and Inductive Logic Programming
Renaud Lecoeuche |
NAACL | 1 |
| 2000 | Finding Comparatively Important Concepts between TextsabstractFinding important concepts is a common task in requirements engineering. For example, it is needed when building models of a domain or organising requirements documents. Since a lot of information is available in textual form, methods to identify important concepts from texts are potentially useful. Traditional methods for finding important concepts from texts rely on the assumption that the most frequent concepts are the most important. We present an approach that does not depend on this assumption. It makes use of two texts to find important concepts comparatively. We show that this approach is viable. It discovers concepts similar to those found by traditional approaches as well as concepts that are not frequent. Finally, we discuss the possibility of extending this work to requirements classification. Renaud Lecoeuche |
ASE | 1 |
| 2000 | Evaluating focus theories for dialogue management
Renaud Lecoeuche, David Stuart Robertson 0001, Catherine Barry, Chris Mellish |
Int. J. Hum. Comput. Stud. | 1 |
| 1999 | Using Focus Rules in Requirements Elicitation Dialogues
Renaud Lecoeuche, Catherine Barry, David Stuart Robertson 0001 |
IJCAI | 1 |