Demo Data from EDBT 2026 / reviewers in the wild / expert
Dimitar Iliyanov Dimitrov
dblp:372/2743
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0003-1308-180XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 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 › narrative understanding
narrative extraction |
0.9 | 1 | 2025 | PolyNarrative: A Multilingual, Multilabel, Multi-domain Dataset for Narrative Extraction from News Articles · ACL (1) 2025 |
Natural language and speech › Information extraction and text analysis
multilingual NLP |
0.3 | 1 | 2025 | PolyNarrative: A Multilingual, Multilabel, Multi-domain Dataset for Narrative Extraction from News Articles · ACL (1) 2025 |
Methods — techniques the papers use, named apart from their topics
multi-label annotation · 0.9dataset construction · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | A Corpus of Persuasion Techniques in Slavic LanguagesabstractPersuasion techniques are powerful rhetorical devices used to sway public opinion in a wide range of media. We present a new corpus of persuasion techniques, focusing on Slavic languages. The corpus contains documents in Bulgarian, Polish, and Russian, annotated with persuasion techniques at the coarse-grained text-span level and fine-grained sentence level. The techniques are drawn from a taxonomy of 25 fine-grained persuasion techniques, grouped under six broad categories of rhetorical persuasion strategies. The corpus contains approximately 7500 text spans from 222 documents that cover topics hotly debated at the national and international levels. We describe the corpus creation process, provide detailed statistics, and examine correlations between topics and persuasion techniques. We use classic ML-based and generative AI-based models to provide baselines and benchmark results for the detection and classification of persuasion techniques at the text-span level and sentence level. Jakub Piskorski, Dimitar Iliyanov Dimitrov, Marina Ernst, Jacek Haneczok, Michal Marcinczuk, Arkadiusz Modzelewski, Roman Yangarber |
LREC | 2 |
| 2025 | PolyNarrative: A Multilingual, Multilabel, Multi-domain Dataset for Narrative Extraction from News ArticlesabstractNikolaos Nikolaidis, Nicolas Stefanovitch, Purificação Silvano, Dimitar Iliyanov Dimitrov, Roman Yangarber, Nuno Guimarães, Elisa Sartori, Ion Androutsopoulos, Preslav Nakov, Giovanni Da San Martino, Jakub Piskorski. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025. Nikolaos Nikolaidis 0004, Nicolas Stefanovitch, Purificação Silvano, Dimitar Iliyanov Dimitrov, Roman Yangarber, Nuno Guimarães, Elisa Sartori, Ion Androutsopoulos, Preslav Nakov, Giovanni Da San Martino, Jakub Piskorski |
ACL (1) | 4 |