Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Tatiana Batura"'
Autor:
Natalia Loukachevitch, Suresh Manandhar, Elina Baral, Igor Rozhkov, Pavel Braslavski, Vladimir Ivanov, Tatiana Batura, Elena Tutubalina
Publikováno v:
Bioinformatics. 39
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 dataset NEREL by introducing domain-specific entity types. NEREL-BIO ann
Publikováno v:
Vestnik NSU. Series: Information Technologies. 19:65-75
Due to the growth of the number of scientific publications, the tasks related to scientific article processing become more actual. Such texts have a special structure, lexical and semantic content that should be taken into account while processing. U
Autor:
Tatiana Batura, Elena Bruches
Publikováno v:
Vestnik NSU. Series: Information Technologies. 19:5-16
We propose a method for scientific terms extraction from the texts in Russian based on weakly supervised learning. This approach doesn't require a large amount of hand-labeled data. To implement this method we collected a list of terms in a semi-auto
Publikováno v:
Scopus-Elsevier
This article describes the original method of automatic summarization of scientific and technical texts based on rhetorical analysis and using topic modeling. The proposed method combines the use of a linguistic knowledge base and machine learning. F
Publikováno v:
Communications in Computer and Information Science ISBN: 9783031122842
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02dcee4f1a0b42f693cdeaeb3b761caa
https://doi.org/10.1007/978-3-031-12285-9_15
https://doi.org/10.1007/978-3-031-12285-9_15
Autor:
Amina Miftahova, Alexander Pugachev, Artem Skiba, Katya Artemova, Tatiana Batura, Pavel Braslavski, Vladimir Ivanov
Publikováno v:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022).
Autor:
Vladimir Ivanov, Ivan Smurov, Veronika Sarkisyan, Vitaly Ivanin, Tatiana Batura, Elena Tutubalina, Ekaterina Artemova
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030726096
AIST
AIST
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. The main challenges of this corpus are: 1) the an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c7692dcf97974e55737472bec1a9400
https://doi.org/10.1007/978-3-030-72610-2_2
https://doi.org/10.1007/978-3-030-72610-2_2
This paper is devoted to the study of methods for information extraction (entity recognition and relation classification) from scientific texts on information technology. Scientific publications provide valuable information into cutting-edge scientif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c27e05ba14c23315290c3d658a5c8e36
http://arxiv.org/abs/2011.09817
http://arxiv.org/abs/2011.09817
Autor:
Sergey Berezin, Alexey Pauls, Yuliya Rubtsova, Tatiana Batura, Ivan Bondarenko, Bair Tuchinov
Publikováno v:
2020 Science and Artificial Intelligence conference (S.A.I.ence).
This paper presents new methods for entity recognition and relation extraction tasks on partially labeled and unlabeled datasets. The proposed methods are based on techniques of semi-supervised, unsupervised and the transfer learning. We use the few-
Autor:
Vladimir Ivanov, Abbyy, Tatiana Batura, Ivan Smurov, Ekaterina Artemova, Vitaly Ivanin, Elena Tutubalina, Veronika Sarkisyan
Publikováno v:
Computational Linguistics and Intellectual Technologies.
In this paper, we present a shared task on core information extraction problems, named entity recognition and relation extraction. In contrast to popular shared tasks on related problems, we try to move away from strictly academic rigor and rather mo