Search Results for author:
Found 12 papers, 7 papers with code
NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition
The first approach follows the token classification schema, in which each token is assigned with a tag.
Entity Linking over Nested Named Entities for Russian
In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction.
Automatic Aspect Extraction from Scientific Texts
Being able to extract from scientific papers their main points, key insights, and other important information, referred to here as aspects, might facilitate the process of conducting a scientific literature review.
Named Entity Inclusion in Abstractive Text Summarization
After that, this model is used to mask named entities in the text and the BART model is trained to reconstruct them.
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL.
TERMinator: A system for scientific texts processing
This paper is devoted to the extraction of entities and semantic relations between them from scientific texts, where we consider scientific terms as entities.
RuNNE-2022 Shared Task: Recognizing Nested Named Entities
In the test set the frequency of all entity types is even.
A system for information extraction from scientific texts in Russian
In this paper, we present a system for information extraction from scientific texts in the Russian language.
NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction.
Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian
This paper is devoted to the study of methods for information extraction (entity recognition and relation classification) from scientific texts on information technology.
RuREBus: a Case Study of Joint Named Entity Recognition and Relation Extraction from e-Government Domain
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, issued by a state agency.
So What's the Plan? Mining Strategic Planning Documents
In this paper we present a corpus of Russian strategic planning documents, RuREBus.
