CoNLLderived

CoNLLderived is a label used in computational linguistics to denote resources and artifacts that have been created from, or are heavily inspired by, datasets and tasks associated with the Conference on Computational Linguistics (CoNLL) and its series of shared tasks. The term covers data, models, evaluation tools, and methodological approaches that originate from CoNLL materials rather than being created independently.

Origins and scope: CoNLL has hosted many shared tasks, including part-of-speech tagging, named entity recognition, parsing, and machine translation. Researchers often generate CoNLLderived resources by applying, adapting, or extending these datasets, annotations (such as CoNLL-U formatted corpora), or evaluation protocols to new languages, domains, or tasks.

Examples: A PoS tagger trained on CoNLL-2000 data and evaluated on a different corpus could be described as CoNLLderived. A dependency parser built and tuned using CoNLL-U annotations, or a benchmark suite derived from CoNLL tests for cross-lingual transfer, would also fall under the term. It can also refer to tooling or scripts that reproduce CoNLL-style preprocessing or evaluation.

Significance and limitations: The term signals compatibility with CoNLL-style experiments and facilitates reproducibility. Potential limitations include licensing restrictions on CoNLL data and the risk that models optimize for CoNLL distributions rather than broader real-world data.

See also: CoNLL, CoNLL-U, shared task; natural language processing datasets; evaluation metrics in NLP.