GuardrailsAI/competitor-check

tl;dr:

This is a dataset largely based on CleanCoNLL with some augmentations.

Details:

Base:

We started with the CoNLL-2003 dataset, a standard NER benchmark containing English and German text annotated with four entity types: person, location, organization, and miscellaneous. For our evaluation, we focused solely on examples containing the ORG (organization) entity, as these are most relevant to competitor detection. We then applied corrections from CleanCoNLL, a 2023 revision by Rücker and Akbik that addresses annotation errors in the original CoNLL-2003. CleanCoNLL corrects 7.0% of labels in the English dataset, adds entity linking annotations, and maintains the original four entity types. This improved dataset enables more accurate evaluation, with top NER models achieving F1-scores up to 97.1%.

Augmentations:

We created two augmented datasets to test specific aspects of competitor detection:

Positive Dataset (with typographical errors): We selected random examples and used the ORG entity as the "competitor" to be detected. We introduced typographical errors to the competitor names by:

• Omission: Removing a letter
• Transposition: Swapping two adjacent letters
• Substitution: Swapping a letter with one found nearby on a US ANSI keyboard layout
• Duplication: Selecting a character at random and doubling it

This dataset tests the guardrail's ability to detect variations of competitor names, which is particularly relevant as our solution does not implement fuzzy matching.

Negative Dataset (with distractors) For the negative dataset, we used the original examples containing ORG entities but created a list of "competitors" by randomly selecting companies from the Fortune 500 index (2024), excluding the actual ORG entity in the text. We set the 'has_competitor' flag to 'false' for all examples in this dataset. This evaluates the guardrail's precision in avoiding false positives when no actual competitors are mentioned.

Citations

@inproceedings{rucker-akbik-2023-cleanconll,
    title = "{C}lean{C}o{NLL}: A Nearly Noise-Free Named Entity Recognition Dataset",
    author = {R{\"u}cker, Susanna  and Akbik, Alan},
    editor = "Bouamor, Houda and Pino, Juan and Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.533",
    doi = "10.18653/v1/2023.emnlp-main.533",
    pages = "8628--8645",
}

@misc{rücker2023cleanconll,
      title={{C}lean{C}o{NLL}: A Nearly Noise-Free Named Entity Recognition Dataset}, 
      author={Susanna R{\"u}cker and Alan Akbik},
      year={2023},
      eprint={2310.16225},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}