(named) entity recognition

MULTICONER is a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions.

The goal of SocialDisNER is the automatic recognition of disease mentions in tweets.

The corpus is a collection of 500 abstracts from Elsevier journal papers related to the biomedical domain collected between 2017 and 2018. It is divided into two disjoined parts: training set (80%) and test set (20%). It is annotated with disabilities and negations and their scope.