# KIND: an Italian Multi-Domain Dataset for Named Entity Recognition

arXiv: https://arxiv.org/abs/2112.15099 · HTML: https://mlsanity.com/paper/2112.15099/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DAWT: Densely Annotated Wikipedia Texts across multiple languages](https://mlsanity.com/paper/1703.00948/) (2017)

## Built on by (most cited first, 2 of 2)

- [CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction](https://mlsanity.com/paper/2403.15322/) (2024)
- [SLIMER-IT: Zero-Shot NER on Italian Language](https://mlsanity.com/paper/2409.15933/) (2024)

## Abstract (arXiv)

In this paper we present KIND, an Italian dataset for Named-entity recognition. It contains more than one million tokens with annotation covering three classes: person, location, and organization. The dataset (around 600K tokens) mostly contains manual gold annotations in three different domains (news, literature, and political discourses) and a semi-automatically annotated part. The multi-domain feature is the main strength of the present work, offering a resource which covers different styles and language uses, as well as the largest Italian NER dataset with manual gold annotations. It represents an important resource for the training of NER systems in Italian. Texts and annotations are freely downloadable from the Github repository.