Create README.md

README.md

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+---
+task_categories:
+- text-classification
+- token-classification
+language:
+- en
+---
+
+# tl;dr:
+
+This is a dataset largely based on CleanCoNLL but 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}
+}
+```