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

被引：0

作者：

Paccosi, Teresa ^{[1
,2
]}

Aprosio, Alessio Palmero ^{[1
]}

机构：

[1] Fdn Bruno Kessler, Via Sommarive 18, Trento, Italy

[2] Univ Trento, Corso Bettini 84, Rovereto, Italy

来源：

LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | 2022年

关键词：

Named-entity recognition; Italian language; Natural Language Processing;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：501 / 507

页数：7

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