CoNLL

CoNLL, short for Conference on Computational Natural Language Learning, is an annual academic conference focused on research at the intersection of natural language processing and machine learning. It is organized by SIGDAT, the Special Interest Group on Data and Text of the Association for Computational Linguistics (ACL). The event features peer‑reviewed papers, tutorials, and demonstrations, and is regarded as a major venue for advances in data‑driven approaches to language understanding.

A distinctive feature of CoNLL is its emphasis on shared tasks, or data challenges. Each year datasets and evaluation protocols are released to enable researchers to benchmark competing approaches under standardized conditions. These tasks have covered areas such as text chunking, named entity recognition, syntactic parsing, semantic role labeling, and more recently coreference resolution and discourse analysis.

Conference proceedings are published in Springer’s Lecture Notes in Computer Science (LNCS). CoNLL has attracted participants from academia and industry and has helped establish widely used benchmark datasets and evaluation metrics, contributing to the reproducibility and comparability of NLP research. The conference has played a central role in the development of both traditional machine learning methods and modern neural approaches.

Since the mid‑1990s, CoNLL has rotated among different countries and institutions, fostering international collaboration and the dissemination of data resources for the NLP community.