Hungarian named entity recognition model with OntoNotes5 + more entity types

• Pretrained model used: SZTAKI-HLT/hubert-base-cc
• Finetuned on NerKor+CARS-ONPP Corpus

Limitations

• max_seq_length = 448

Training data

The underlying corpus, NerKor+CARS-OntoNotes++ , was derived from NYTK-NerKor , a Hungarian gold standard named entity annotated corpus containing about 1 million tokens. It includes a small addition of 12k tokens of text (individual sentences) concerning motor vehicles (cars, buses, motorcycles) from the news archive of hvg.hu . While the annotation in NYTK-NerKor followed the CoNLL2002 labelling standard with just four NE categories ( PER , LOC , MISC , ORG ), this version of the corpus features over 30 entity types, including all entity types used in the [OntoNotes 5.0] English NER annotation. The new annotation elaborates on subtypes of the LOC and MISC entity types, and includes annotation for non-names like times and dates, quantities, languages and nationalities or religious or political groups. The annotation was elaborated with further entity subtypes not present in the Ontonotes 5 annotation (see below).

Tags derived from the OntoNotes 5.0 annotation

Names are annotated according to the following set of types:

The following are also annotated in a style similar to names:

Additional tags (not in OntoNotes 5)

Further subtypes of names of type MISC :

Further non-name entities:

If you use this model, please cite:

@inproceedings{novak-novak-2022-nerkor,
    title = "{N}er{K}or+{C}ars-{O}nto{N}otes++",
    author = "Nov{\'a}k, Attila  and
      Nov{\'a}k, Borb{\'a}la",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.203",
    pages = "1907--1916",
    abstract = "In this paper, we present an upgraded version of the Hungarian NYTK-NerKor named entity corpus, which contains about twice as many annotated spans and 7 times as many distinct entity types as the original version. We used an extended version of the OntoNotes 5 annotation scheme including time and numerical expressions. NerKor is the newest and biggest NER corpus for Hungarian containing diverse domains. We applied cross-lingual transfer of NER models trained for other languages based on multilingual contextual language models to preannotate the corpus. We corrected the annotation semi-automatically and manually. Zero-shot preannotation was very effective with about 0.82 F1 score for the best model. We also added a 12000-token subcorpus on cars and other motor vehicles. We trained and release a transformer-based NER tagger for Hungarian using the annotation in the new corpus version, which provides similar performance to an identical model trained on the original version of the corpus.",
}