Demo Data from SIGIR 2026 / reviewers in the wild / expert
Imed Keraghel
dblp:367/3248
· DBLP profile ↗
5ranked-venue papers
4as first author
5since 2021 · last 2026
0009-0008-4943-2556ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 4 · 3 first-author · 4 since 2021Artificial intelligence and machine learning · 3 · 2 first-author · 3 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.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 50% Data mining · 50% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining
clustering |
1.0 | 1 | 2026 | Named Entity-Driven Graph Smoothing to Enhance Pretrained Document Embeddings in Clustering Tasks · SIGIR 2026 |
Data mining › clustering
document clustering |
1.0 | 1 | 2026 | Named Entity-Driven Graph Smoothing to Enhance Pretrained Document Embeddings in Clustering Tasks · SIGIR 2026 |
Information retrieval › document processing › document analysis › document representation
document embedding |
1.0 | 1 | 2026 | Named Entity-Driven Graph Smoothing to Enhance Pretrained Document Embeddings in Clustering Tasks · SIGIR 2026 |
Information retrieval › document processing › document analysis
document representation |
1.0 | 1 | 2026 | Named Entity-Driven Graph Smoothing to Enhance Pretrained Document Embeddings in Clustering Tasks · SIGIR 2026 |
Data mining › text mining › information extraction
named entity recognition |
0.3 | 1 | 2026 | Named Entity-Driven Graph Smoothing to Enhance Pretrained Document Embeddings in Clustering Tasks · SIGIR 2026 |
Information retrieval
text analysis |
0.3 | 1 | 2026 | Named Entity-Driven Graph Smoothing to Enhance Pretrained Document Embeddings in Clustering Tasks · SIGIR 2026 |
Methods — techniques the papers use, named apart from their topics
pre-trained embeddings · 1.0graph smoothing · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Optimizing Dimensionality Reduction Hyperparameters for Improved Clustering Performance
Imed Keraghel, Mohamed Nadif |
ICPR (14) | 1 |
| 2026 | Named Entity-Driven Graph Smoothing to Enhance Pretrained Document Embeddings in Clustering TasksabstractInternational audience Imed Keraghel, Mohamed Nadif |
SIGIR | 1 |
| 2025 | Graph-Convolutional Networks: Named Entity Recognition and Large Language Model Embedding in Document Clustering
Imed Keraghel, Mohamed Nadif |
ECIR (2) | 1 |
| 2025 | Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM
Noor Khalal, Abdallah Alaa-Eddine Djamai, Imed Keraghel, Mohamed Nadif |
IDA | 3 |
| 2024 | Beyond Words: A Comparative Analysis of LLM Embeddings for Effective Clustering
Imed Keraghel, Stanislas Morbieu, Mohamed Nadif |
IDA (1) | 1 |