Demo Data from EDBT 2026 / reviewers in the wild / expert
Cristea Petru-Theodor
dblp:425/9051
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 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 |
Language models and text generation · 67% Information extraction and text analysis · 33% | |
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 50% Recommender systems · 50% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › text generation › text simplification
lexical simplification |
0.9 | 1 | 2025 | RALS: Resources and Baselines for Romanian Automatic Lexical Simplification · EMNLP 2025 |
Natural language and speech › Language models and text generation › text generation
text simplification |
0.9 | 1 | 2025 | RALS: Resources and Baselines for Romanian Automatic Lexical Simplification · EMNLP 2025 |
Recommender systems › personalized ranking
pairwise ranking |
0.3 | 1 | 2025 | RALS: Resources and Baselines for Romanian Automatic Lexical Simplification · EMNLP 2025 |
Information retrieval
ranking |
0.3 | 1 | 2025 | RALS: Resources and Baselines for Romanian Automatic Lexical Simplification · EMNLP 2025 |
Methods — techniques the papers use, named apart from their topics
pairwise ranking approximation · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | RALS: Resources and Baselines for Romanian Automatic Lexical SimplificationabstractWe introduce the first dataset that jointly covers both lexical complexity prediction (LCP) annotations and lexical simplification (LS) for Romanian, along with a comparison of lexical simplification approaches.We propose a methodology for ordering simplification suggestions using a pairwise ranking approximation method, arranging candidates from simple to complex based on a separate set of human judgments.In addition, we provide human lexical complexity annotations for 3,921 word samples in context.Finally, we explore several novel pipelines for complexity prediction and simplification and present the first text simplification system for Romanian. Fabian Anghel, Cristea Petru-Theodor, Claudiu Creanga, Sergiu Nisioi |
EMNLP | 2 |