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

TL;DR

This paper introduces Razmecheno, a new Russian NER dataset from diary texts during Perestroika, addressing the lack of historical and literary domain data, and evaluates its utility with existing NER tools and models.

Contribution

The creation and release of Razmecheno, a novel annotated NER dataset from Russian diaries, filling a gap in historical, literary, and low-resource language datasets.

Findings

Existing NER tools perform variably on Razmecheno.

Fine-tuning pre-trained encoders improves NER performance on the dataset.

Razmecheno supports research in cross-lingual and low-resource NER.

Abstract

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.