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--- |
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task_categories: |
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- text-classification |
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- token-classification |
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language: |
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- en |
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--- |
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# tl;dr: |
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This is a dataset largely based on CleanCoNLL with some augmentations. |
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# Details: |
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## Base: |
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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. |
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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%. |
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## Augmentations: |
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We created two augmented datasets to test specific aspects of competitor detection: |
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Positive Dataset (with typographical errors): |
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We selected random examples and used the ORG entity as the "competitor" to be detected. We introduced typographical errors to the competitor names by: |
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- Omission: Removing a letter |
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- Transposition: Swapping two adjacent letters |
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- Substitution: Swapping a letter with one found nearby on a US ANSI keyboard layout |
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- Duplication: Selecting a character at random and doubling it |
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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. |
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Negative Dataset (with distractors) |
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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. |
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# Citations |
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``` |
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@inproceedings{rucker-akbik-2023-cleanconll, |
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title = "{C}lean{C}o{NLL}: A Nearly Noise-Free Named Entity Recognition Dataset", |
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author = {R{\"u}cker, Susanna and Akbik, Alan}, |
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editor = "Bouamor, Houda and Pino, Juan and Bali, Kalika", |
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booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2023", |
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address = "Singapore", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.emnlp-main.533", |
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doi = "10.18653/v1/2023.emnlp-main.533", |
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pages = "8628--8645", |
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} |
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@misc{rücker2023cleanconll, |
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title={{C}lean{C}o{NLL}: A Nearly Noise-Free Named Entity Recognition Dataset}, |
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author={Susanna R{\"u}cker and Alan Akbik}, |
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year={2023}, |
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eprint={2310.16225}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
