metadata

language:
  - en
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
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

Dataset Summary

FIN NER dataset formatted in a part of TNER project. FIN dataset contains training (FIN5) and test (FIN3) only, so we randomly sample a half size of test instances from the training set to create validation set.

Entity Types: ORG, LOC, PER, MISC

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,
  "B-PER": 1,
  "B-LOC": 2,
  "B-ORG": 3,
  "B-MISC": 4,
  "I-PER": 5,
  "I-LOC": 6,
  "I-ORG": 7,
  "I-MISC": 8
}

Data Splits

name	train	validation	test
fin	1014	303	150

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",
}