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 nested into another entity. This way, the
named entity "The Yermolova Theatre" of type "organization" houses another
entity "Yermolova" of type "person". We adopt the Russian NEREL dataset for the
RuNNE Shared Task. NEREL comprises news texts written in the Russian language
and collected from the Wikinews portal. The annotation schema includes 29
entity types. The nestedness of named entities in NEREL reaches up to six
levels. The RuNNE Shared Task explores two setups. (i) In the general setup all
entities occur more or less with the same frequency. (ii) In the few-shot setup
the majority of entity types occur often in the training set. However, some of
the entity types are have lower frequency, being thus challenging to recognize.
In the test set the frequency of all entity types is even.
This paper reports on the results of the RuNNE Shared Task. Overall the
shared task has received 156 submissions from nine teams. Half of the
submissions outperform a straightforward BERT-based baseline in both setups.
This paper overviews the shared task setup and discusses the submitted systems,
discovering meaning insights for the problem of nested NER. The links to the
evaluation platform and the data from the shared task are available in our
github repository: this https URL.

本研究报告基于俄语 NEREL 数据集在 RuNNE 共享任务中进行嵌套命名实体识别，该任务涉及 29 种实体类型，覆盖 6 级嵌套，共有 9 个团队提交了 156 个结果，结果表明大部分结果超过了 BERT 的基线表现。