NEREL: A Russian Dataset with Nested Named Entities, Relations and Events

Natalia Loukachevitch, Ekaterina Artemova, Tatiana Batura, Pavel Braslavski, Ilia Denisov, Vladimir Ivanov, Suresh Manandhar, Alexander Pugachev, Elena Tutubalina

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

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 contains 56K annotated named entities and 39K annotated relations. Its important difference from previous datasets is annotation of nested named entities, as well as relations within nested entities and at the discourse level. NEREL can facilitate development of novel models that can extract relations between nested named entities, as well as relations on both sentence and document levels. NEREL also contains the annotation of events involving named entities and their roles in the events. The NEREL collection is available via https://github.com/nerel-ds/NEREL.

Язык оригиналаанглийский
Название основной публикацииInternational Conference Recent Advances in Natural Language Processing, RANLP 2021
Подзаголовок основной публикацииDeep Learning for Natural Language Processing Methods and Applications - Proceedings
РедакторыGalia Angelova, Maria Kunilovskaya, Ruslan Mitkov, Ivelina Nikolova-Koleva
ИздательIncoma Ltd
Страницы876-885
Число страниц10
ISBN (электронное издание)9789544520724
DOI
СостояниеОпубликовано - 2021
СобытиеInternational Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021 - Virtual, Online
Продолжительность: 1 сен 20213 сен 2021

Серия публикаций

НазваниеInternational Conference Recent Advances in Natural Language Processing, RANLP
ISSN (печатное издание)1313-8502

Конференция

КонференцияInternational Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021
ГородVirtual, Online
Период01.09.202103.09.2021

Предметные области OECD FOS+WOS

  • 1.02 КОМПЬЮТЕРНЫЕ И ИНФОРМАЦИОННЫЕ НАУКИ
  • 2.02.IQ ИНЖЕНЕРИЯ, ЭЛЕКТРИЧЕСКАЯ И ЭЛЕКТРОННАЯ

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