The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations an
The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia.
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example: [PER Wolff] , currently a journalist in [LOC Argentina]
The Dialectal Arabic Datasets contain four dialects of Arabic, Etyptian (EGY), Levantine (LEV), Gulf (GLF), and Maghrebi (MGR). Each dataset consists of a set of 350 manually
The Wikicorpus is a trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with
The DaNE dataset has been annotated with Named Entities for PER, ORG and LOC by the Alexandra Institute. It is a reannotation of the UD-DDT (Universal Dependency - Danish Depe
Mac-Morpho is a corpus of Brazilian Portuguese texts annotated with part-of-speech tags. Its first version was released in 2003 , and since then, two revisions have been ma
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand. It offers five layers of linguistic
ThaiNER (v1.3) is a 6,456-sentence named entity recognition dataset created from expanding the 2,258-sentence [unnamed dataset](http://pioneer.chula.ac.th/~awirote/Data-Nutcha
The hr500k training corpus contains about 500,000 tokens manually annotated on the levels of tokenisation, sentence segmentation, morphosyntactic tagging, lemmatisation and n
SETimes_sr is a Serbian dataset annotated for morphosyntactic information and named entities. The dataset contains 3177 training samples, 395 validation samples and 319 test
The dataset contains 5462 training samples, 711 validation samples and 725 test samples. Each sample represents a sentence and includes the following features: sentence ID ('
The dataset contains 6339 training samples, 815 validation samples and 785 test samples. Each sample represents a sentence and includes the following features: sentence ID ('