Igor Rozhkov

Lomonosov Moscow State University

Department of Algorithmic Languages

I am a second-year PhD student at Laboratory for Information Resources Analysis, Research Computing Center of Lomonosov Moscow State University, advised by Natalia Loukachevitch. My research focuses on Nested Named Entity Recognition and Relation Extraction.

I study how language models can be applied to complex information extraction tasks, particularly recognizing nested entity mentions. I am also interested in domain and language adaptation, LLMs (for nested named entity recognition and relation extraction), knowledge graphs, low-resource and few-shot NER, biomedical NLP.

Selected

2026

Learning Nested Named Entity Recognition from Flat Annotations

Igor Rozhkov and Natalia V Loukachevitch

In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop), Mar 2026

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Nested named entity recognition identifies entities contained within other entities, but requires expensive multi-level annotation. While flat NER corpora exist abundantly, nested resources remain scarce. We investigate whether models can learn nested structure from flat annotations alone, evaluating four approaches: string inclusions (substring matching), entity corruption (pseudo-nested data), flat neutralization (reducing false negative signal), and a hybrid fine-tuned + LLM pipeline. On NEREL, a Russian benchmark with 29 entity types where 21% of entities are nested, our best combined method achieves 26.37% inner F1, closing 40% of the gap to full nested supervision. Code is available at https://github.com/fulstock/Learning-from-Flat-Annotations.
BioASQ at CLEF2026: The Fourteenth Edition of the Large-Scale Biomedical Semantic Indexing and Question Answering Challenge

Anastasios Nentidis, Georgios Katsimpras, Anastasia Krithara, and 18 more authors

In Advances in Information Retrieval, 2026

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Over the past thirteen years, the large-scale biomedical semantic indexing and question-answering challenge (BioASQ) has consistently driven the advancement of methods and tools that enhance access to the rapidly growing body of scientific resources in the biomedical domain. BioASQ offers a unique common testbed where research teams worldwide can evaluate and compare innovative approaches for accessing biomedical knowledge by organizing shared tasks on an annual basis and providing respective benchmark datasets that represent the real information needs of biomedical experts.

2025

Methods for Recognizing Nested Terms

Igor Rozhkov and Natalia Loukachevitch

May 2025

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In this paper, we describe our participation in the RuTermEval competition devoted to extracting nested terms. We apply the Binder model, which was previously successfully applied to the recognition of nested named entities, to extract nested terms. We obtained the best results of term recognition in all three tracks of the RuTermEval competition. In addition, we study the new task of recognition of nested terms from flat training data annotated with terms without nestedness. We can conclude that several approaches we proposed in this work are viable enough to retrieve nested terms effectively without nested labeling of them.

2024

NEREL: A Russian Information Extraction Dataset with Rich Annotation for Nested Entities, Relations, and Wikidata Entity Links

Natalia Loukachevitch, Ekaterina Artemova, Tatiana Batura, and 8 more authors

Language Resources and Evaluation, Jun 2024

DOI

2023

NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities

Natalia Loukachevitch, Suresh Manandhar, Elina Baral, and 5 more authors

Bioinformatics, 2023
Machine Reading Comprehension Model in Domain-Transfer Task

I. S. Rozhkov and N. V. Loukachevitch

Lobachevskii Journal of Mathematics, Aug 2023

DOI
Prompts in Few-Shot Named Entity Recognition

I. S. Rozhkov and N. V. Loukachevitch

Pattern Recognition and Image Analysis, Jun 2023

DOI

2022

RuNNE-2022 Shared Task: Recognizing Nested Named Entities

Ekaterina Artemova, Maxim Zmeev, Natalia Loukachevitch, and 4 more authors

In COMPUTATIONAL LINGUISTICS AND INTELLECTUAL TECHNOLOGIES, Jun 2022

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The RuNNE Shared Task approaches the problem of nested named entity recognition. The annotation schema is designed in such a way, that an entity may partially overlap or even be nested into another entity. This way, the named entity "The Yermolova Theatre" of type "organization" houses another entity "Yermolova" of type "person". We adopt the Russian NEREL dataset for the RuNNE Shared Task. NEREL comprises news texts written in the Russian language and collected from the Wikinews portal. The annotation schema includes 29 entity types. The nestedness of named entities in NEREL reaches up to six levels. The RuNNE Shared Task explores two setups. (i) In the general setup all entities occur more or less with the same frequency. (ii) In the few-shot setup the majority of entity types occur often in the training set. However, some of the entity types are have lower frequency, being thus challenging to recognize. In the test set the frequency of all entity types is even. This paper reports on the results of the RuNNE Shared Task. Overall the shared task has received 156 submissions from nine teams. Half of the submissions outperform a straightforward BERT-based baseline in both setups. This paper overviews the shared task setup and discusses the submitted systems, discovering meaning insights for the problem of nested NER. The links to the evaluation platform and the data from the shared task are available in our github repository: https://github.com/dialogue-evaluation/RuNNE.
Machine Reading Comprehension Model in RuNNE Competition

Lomonosov Moscow State University, Moscow, Russia, Igor Rozhkov, Natalia Loukachevitch, and 1 more author

In Computational Linguistics and Intellectual Technologies, Jun 2022

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The paper studies machine reading comprehension model (MRC) (Li et al., 2020) in its application to extracting nested named entities (nested NER) in the RuNNE-2022 evaluation (Artemova et al., 2022). The model transforms named entity recognition tasks to a question-answering task. In this paper we compare several approaches to formulating ”questions” for the MRC model such as entity type names (keywords), entity type definitions, most frequent examples for the train set, combinations of definitions and examples. We found that using two most frequent examples from the training set is comparable in quality of nested NER with gathering qualitative definitions from different dictionaries, which is much more complicated. In the RuNNE evaluation, the MRC model obtained the best results among models without any manual work (rules or additional manual annotation of texts).