The vast majority of existing datasets for Named Entity Recognition (NER) are
built primarily on news, research papers and Wikipedia with a few exceptions,
created from historical and literary texts. What is more, English is the main
source for data for further labelling. This paper aims to fill in multiple gaps
by creating a novel dataset "Razmecheno", gathered from the diary texts of the
project "Prozhito" in Russian. Our dataset is of interest for multiple research
lines: literary studies of diary texts, transfer learning from other domains,
low-resource or cross-lingual named entity recognition. Razmecheno comprises
1331 sentences and 14119 tokens, sampled from diaries, written during the
Perestroika. The annotation schema consists of five commonly used entity tags:
person, characteristics, location, organisation, and facility. The labelling is
carried out on the crowdsourcing platfrom Yandex.Toloka in two stages. First,
workers selected sentences, which contain an entity of particular type. Second,
they marked up entity spans. As a result 1113 entities were obtained. Empirical
evaluation of Razmecheno is carried out with off-the-shelf NER tools and by
fine-tuning pre-trained contextualized encoders. We release the annotated
dataset for open access.

该研究旨在填补以新闻、研究论文、维基百科为主的命名实体识别数据集的多个空白领域，通过在俄语口述日记文本中创建名为 “Razmecheno” 的新数据集，该数据集包含 1331 个句子和 14119 个标记，并标记了人物，特征，位置，组织和设施等五种命名实体识别的常用实体类型。我们使用众包平台 Yandex.Toloka 进行了数据标注，并通过 NER 工具和微调预训练的上下文编码器进行了实证评估。