NER-g

Datasets used in the articles Context-sensitive Gender Inference of Named Entities in Text and Gender tagging of named entities using retrieval-assisted multi-context aggregation: An unsupervised approach.

Datasets

This repo contains four datasets:

1. CoNLL-g
2. Wiki-g
3. IEER-g
4. Textbook-g

NER tags for these datasets can be obtained from the sources listed below:

1. CoNLL
2. Wiki
3. IEER
4. Textbook NER (gold standard subset)

Format

The files are in CoNLL format: each line contains one token and its corresponding gender label separated by a tab. Document boundaries are denoted by blank lines.

Tag set:

1. O - Out (not part of PERSON named entity)
2. F - Female
3. M - Male
4. A - Ambiguous

Pre-processing & Annotation

Annotated using the IO tagging scheme.

For details, please refer to Section 3 of the article.

Citing

If you find this dataset useful, please cite:

@article{das2023gender,
  title={Gender tagging of named entities using retrieval-assisted multi-context aggregation: An unsupervised approach},
  author={Das, Sudeshna and Paik, Jiaul H},
  journal={Journal of the Association for Information Science and Technology},
  volume={74},
  number={4},
  pages={461--475},
  year={2023},
  doi="https://doi.org/10.1002/asi.24735",
  url="https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/asi.24735"
  publisher={Wiley Online Library}
}

@article{DAS2021102423,
title = "Context-sensitive gender inference of named entities in text",
journal = "Information Processing & Management",
volume = "58",
number = "1",
pages = "102423",
year = "2021",
issn = "0306-4573",
doi = "https://doi.org/10.1016/j.ipm.2020.102423",
url = "http://www.sciencedirect.com/science/article/pii/S0306457320309171",
author = "Sudeshna Das and Jiaul H Paik"
}