22 citations · 36 across the 7 of their papers we have counts for
9 papers
Named Entity Inclusion in Abstractive Text Summarization
Sergey Berezin, Tatiana Batura
We address the named entity omission - the drawback of many current abstractive text summarizers. We suggest a custom pretraining objective to enhance the model's attention on the…
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
Natalia Loukachevitch, Suresh Manandhar, Elina Baral +5
This paper describes NEREL-BIO -- an annotation scheme and corpus of PubMed abstracts in Russian and smaller number of abstracts in English. NEREL-BIO extends the general domain da…
TERMinator: A system for scientific texts processing
Elena Bruches, Olga Tikhobaeva, Yana Dementyeva +1
This paper is devoted to the extraction of entities and semantic relations between them from scientific texts, where we consider scientific terms as entities. In this paper, we pre…
RuNNE-2022 Shared Task: Recognizing Nested Named Entities
Ekaterina Artemova, Maxim Zmeev, Natalia Loukachevitch +4
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 ne…
A system for information extraction from scientific texts in Russian
Elena Bruches, Anastasia Mezentseva, Tatiana Batura
In this paper, we present a system for information extraction from scientific texts in the Russian language. The system performs several tasks in an end-to-end manner: term recogni…
NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
Natalia Loukachevitch, Ekaterina Artemova, Tatiana Batura +6
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it co…
Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian
Elena Bruches, Alexey Pauls, Tatiana Batura +1
This paper is devoted to the study of methods for information extraction (entity recognition and relation classification) from scientific texts on information technology. Scientifi…
RuREBus: a Case Study of Joint Named Entity Recognition and Relation Extraction from e-Government Domain
Vitaly Ivanin, Ekaterina Artemova, Tatiana Batura +4
We show-case an application of information extraction methods, such as named entity recognition (NER) and relation extraction (RE) to a novel corpus, consisting of documents, issue…
So What's the Plan? Mining Strategic Planning Documents
Ekaterina Artemova, Tatiana Batura, Anna Golenkovskaya +5
In this paper we present a corpus of Russian strategic planning documents, RuREBus. This project is grounded both from language technology and e-government perspectives. Not only n…