Demo Data from EDBT 2026 / reviewers in the wild / expert
Clément Christophe
dblp:219/7188
· DBLP profile ↗
4ranked-venue papers
2as first author
4since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4 · 2 first-author · 4 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
3 papers |
Deep learning architectures and training · 33% Representation and self-supervised learning · 33% Information extraction and text analysis · 16% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 7 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training › sequence modeling › sequence generation
sequence-to-sequence generation |
1.0 | 1 | 2026 | Cross-Examination Framework: A Task-Agnostic Diagnostic for Information Fidelity in Text-to-Text Generation · ACL (1) 2026 |
Medical and health informatics › biomedical natural language processing
medical language model |
0.9 | 1 | 2025 | Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency · EMNLP 2025 |
Machine learning › Representation and self-supervised learning › representation learning › representation geometry
embedding geometry |
0.5 | 1 | 2021 | Monitoring geometrical properties of word embeddings for detecting the emergence of new topics · EMNLP (1) 2021 |
Natural language and speech › Information extraction and text analysis › knowledge discovery from text
topic detection |
0.5 | 1 | 2021 | Monitoring geometrical properties of word embeddings for detecting the emergence of new topics · EMNLP (1) 2021 |
Machine learning › Representation and self-supervised learning › word representation
word embedding |
0.5 | 1 | 2021 | Monitoring geometrical properties of word embeddings for detecting the emergence of new topics · EMNLP (1) 2021 |
Natural language and speech › Language models and text generation › trustworthy language model › large language model reliability › factuality
factual consistency |
0.3 | 1 | 2026 | Cross-Examination Framework: A Task-Agnostic Diagnostic for Information Fidelity in Text-to-Text Generation · ACL (1) 2026 |
Machine learning › Trustworthy machine learning
fairness and bias |
0.3 | 1 | 2025 | Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency · EMNLP 2025 |
Methods — techniques the papers use, named apart from their topics
bias analysis · 1.7large language model · 1.0cross-examination · 1.0word embeddings · 0.5geometric monitoring · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Cross-Examination Framework: A Task-Agnostic Diagnostic for Information Fidelity in Text-to-Text GenerationabstractTathagata Raha, Clement Christophe, Nada Saadi, Hamza A Javed, Marco AF Pimentel, Ronnie Rajan, Praveenkumar Kanithi. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026. Tathagata Raha, Clément Christophe, Nada Saadi, Hamza Javed, Marco A. F. Pimentel, Ronnie Rajan, Praveenkumar Kanithi |
ACL (1) | 2 |
| 2025 | Building Trust in Clinical LLMs: Bias Analysis and Dataset TransparencyabstractSvetlana Maslenkova, Clement Christophe, Marco AF Pimentel, Tathagata Raha, Muhammad Umar Salman, Ahmed Al Mahrooqi, Avani Gupta, Shadab Khan, Ronnie Rajan, Praveenkumar Kanithi. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. Svetlana Maslenkova, Clément Christophe, Marco A. F. Pimentel, Tathagata Raha, Muhammad Umar Salman, Ahmed Al-Mahrooqi, Avani Gupta, Shadab Khan, Ronnie Rajan, Praveen K. Kanithi |
EMNLP | 2 |
| 2021 | Monitoring geometrical properties of word embeddings for detecting the emergence of new topicsabstractSlow emerging topic detection is a task between event detection, where we aggregate behaviors of different words on short period of time, and language evolution, where we monitor their long term evolution.In this work, we tackle the problem of early detection of slowly emerging new topics.To this end, we gather evidence of weak signals at the word level.We propose to monitor the behavior of words representation in an embedding space and use one of its geometrical properties to characterize the emergence of topics.As evaluation is typically hard for this kind of task, we present a framework for quantitative evaluation.We show positive results that outperform state-ofthe-art methods on two public datasets of press and scientific articles. Clément Christophe, Julien Velcin, Jairo Cugliari, Manel Boumghar, Philippe Suignard |
EMNLP (1) | 1 |
| 2021 | Change detection in textual classification with unexpected dynamics
Clément Christophe, Julien Velcin, Jairo Cugliari, Philippe Suignard, Manel Boumghar |
Expert Syst. Appl. | 1 |