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Recognition of Named Entities in the Russian Subcorpus Google Books Ngram
Bochkarev V.V., Khristoforov S.V., Shevlyakova A.V.
was usually input to the recognizer to solve the task of named entities recognition. In this paper
On biomedical named entity recognition: Experiments in interlingual transfer for clinical and social media texts
Miftahutdinov Z., Alimova I., Tutubalina E.
-of-the-art performance in biomedical named entity recognition (bioNER), much research shares one limitation: models
Dictionary and pattern-based recognition of organization names in Russian news texts
Solovyev V., Gareev R., Ivanov V., Serebryakov S., Vassilieva N.
of ambiguous names among dictionary entries. The second recognition approach is based on usage of local context
End-to-end deep framework for disease named entity recognition using social media data
propose to address a challenging problem by applying modern deep neural networks for disease named entity
Recognition of Sarcastic Sentences in the Task of Sentiment Analysis
Dolbin, A. V., Rozaliev, V., Orlova, Y., Fomenkov, S.
This article is devoted to the sarcasm recognition in the text written in a natural language
RuREBus: A Case Study of Joint Named Entity Recognition and Relation Extraction from E-Government Domain
Ivanin V., Artemova E., Batura T., Ivanov V., Sarkisyan V., Tutubalina E., Smurov I.
We show-case an application of information extraction methods, such as named entity recognition
Dictionary and pattern-based recognition of organization names in Russian news texts
Solovyev V., Gareev R., Ivanov V., Serebryakov S., Vassilieva N.
of ambiguous names among dictionary entries. The second recognition approach is based on usage of local context
Rurebus-2020 shared task: Russian relation extraction for business
Ivanin V.A., Artemova E.L., Batura T.V., Ivanov V.V., Sarkisyan V.V., Tutubalina E.V., Smurov I.M.
information extraction problems, named entity recognition and relation extraction. In contrast to popular
Towards Text Processing System for Emergency Event Detection in the Arctic Zone
Deviatkin Dmitriy, Shelmanov Artem
of documents related to emergencies in the Arctic region, text parsing including named entity recognition
Development of methods for extracting information from pharmacy line using conditional random fields
Molodchenkov A.I., Nikolaev A.A., Mitrokhina E.A.
to extract the full name of the drug, manufacturer, form of issue, dosage, number of pieces in a package

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