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

Teresa Paccosi, Alessio Palmero Aprosio


Abstract
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.
Anthology ID:
2022.lrec-1.52
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
501–507
Language:
URL:
https://aclanthology.org/2022.lrec-1.52
DOI:
Bibkey:
Cite (ACL):
Teresa Paccosi and Alessio Palmero Aprosio. 2022. KIND: an Italian Multi-Domain Dataset for Named Entity Recognition. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 501–507, Marseille, France. European Language Resources Association.
Cite (Informal):
KIND: an Italian Multi-Domain Dataset for Named Entity Recognition (Paccosi & Palmero Aprosio, LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.52.pdf
Code
 dhfbk/kind
Data
KIND