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The vast majority of existing datasets for Named Entity Recognition (NER) arebuilt primarily on news, research papers and Wikipedia with a few exceptions,created from historical and literary texts. What is more, English is the mainsource for data for further labelling. This paper aims to fill in multiple gapsby creating a novel dataset "Razmecheno", gathered from the diary texts of theproject "Prozhito" in Russian. Our dataset is of interest for multiple researchlines: literary studies of diary texts, transfer learning from other domains,low-resource or cross-lingual named entity recognition. Razmecheno comprises1331 sentences and 14119 tokens, sampled from diaries, written during thePerestroika. The annotation schema consists of five commonly used entity tags:person, characteristics, location, organisation, and facility. The labelling iscarried 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. Empiricalevaluation of Razmecheno is carried out with off-the-shelf NER tools and byfine-tuning pre-trained contextualized encoders. We release the annotateddataset for open access.

Razmecheno: Named Entity Recognition from Digital Archive of Diaries "Prozhito"