metadata

language:
  - en
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
pretty_name: FIN

Dataset Card for "tner/fin"

Dataset Description

Repository: T-NER
Paper: https://aclanthology.org/U15-1010.pdf
Dataset: FIN
Domain: Financial News
Number of Entity: 4 (ORG, LOC, PER, MISC)

Dataset Summary

FIN NER dataset formatted in a part of TNER project. Original FIN dataset contains two variants of datasets, FIN3 and FIN5 where the FIN3 is the test set, while FIN5 is the training set. We take same amount of instances randomly from the training set and create a validation set with the subset.

Dataset Structure

Data Instances

An example of train looks as follows.

{
    "tags": [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    "tokens": ["1", ".", "1", ".", "4", "Borrower", "engages", "in", "criminal", "conduct", "or", "is", "involved", "in", "criminal", "activities", ";"]
}

Label ID

The label2id dictionary can be found at here.

{
    "O": 0,
    "I-ORG": 1,
    "I-LOC": 2,
    "I-PER": 3,
    "I-MISC": 4
}

Data Splits

name	train	validation	test
fin	861	303	303

Citation Information

@inproceedings{salinas-alvarado-etal-2015-domain,
    title = "Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment",
    author = "Salinas Alvarado, Julio Cesar  and
      Verspoor, Karin  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://aclanthology.org/U15-1010",
    pages = "84--90",
}