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Extended entity recognition#

There can be cases when the default four-type NER does not suffice, one needs a more fine-grained entity type system. Attila Novak developed a corpus and an entity recognition system consisting more than 30 entity types. We provide easy integration with his tool.

Load#

Loading the model can be achieved by adding the nerpp component.

import huspacy.integrations

nlp.add_pipe("nerpp")
Please note that using this component requires transformers, torch and spacy-alignments to be installed. Installing HuSpaCy with trf extras installs all these dependencies: pip install huspacy[trf]

Get entity annotations#

The nerpp components stores entities as spans on the document under the "ents" key:

doc = nlp("A Ford Focus egy alsó-középkategóriás családi autó")
print(doc.spans["ents"])
print(doc.spans["ents"][0].label_)

gives

[Ford Focus]
CAR

Citing#

If you use this component, please cite:

@InProceedings{novak-novak:2022:LREC,
  author    = {Nov{\'{a}}k, Attila  and  Nov{\'{a}}k, Borb{\'{a}}la},
  title     = {NerKor+Cars-OntoNotes++},
  booktitle      = {Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022)},
  month          = {June},
  year           = {2022},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  pages     = {1907--1916},
    url       = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.203.pdf}
}
Last update: January 3, 2024