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

arXiv: https://arxiv.org/abs/2201.09997 · HTML: https://mlsanity.com/paper/2201.09997/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation](https://mlsanity.com/paper/2004.01401/) (2020)
- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning](https://mlsanity.com/paper/1801.09851/) (2018)
- [HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition](https://mlsanity.com/paper/2008.07347/) (2020)
- [NorNE: Annotating Named Entities for Norwegian](https://mlsanity.com/paper/1911.12146/) (2019)
- [A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text](https://mlsanity.com/paper/1711.07010/) (2017)
- [NEREL: A Russian Dataset with Nested Named Entities, Relations and Events](https://mlsanity.com/paper/2108.13112/) (2021)
- [RUBERT: A Bilingual Roman Urdu BERT Using Cross Lingual Transfer Learning](https://mlsanity.com/paper/2102.11278/) (2021)

## Abstract (arXiv)

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.