Apart from yearly special themes, CoNLL accepts contributions about language learning topics, including, but not limited to:
- Computational models of human language acquisition
- Computational models of the origins and evolution of language
- Machine learning methods applied to natural language processing tasks (speech processing, phonology, morphology, syntax, semantics, discourse processing, language engineering applications)
- Symbolic learning methods (Rule Induction and Decision Tree Learning, Lazy Learning, Inductive Logic Programming, Analytical Learning, Transformation-based Error-driven Learning)
- Biologically-inspired methods (Neural Networks, Evolutionary Computing)
- Statistical methods (Bayesian Learning, HMM, maximum entropy, SNoW, Support Vector Machines)
- Reinforcement Learning
- Active learning, ensemble methods, meta-learning
- Computational Learning Theory analyses of language learning
- Empirical and theoretical comparisons of language learning methods
- Models of induction and analogy in Linguistics
Since 1999, CoNLL has included a shared task in which training and test data is provided by the organizers which allows participating systems to be evaluated and compared in a systematic way. Descriptions of the participating systems and an evaluation of their performances are presented both at the conference and in the proceedings.
A list of all editions of the conference with links to the conference home pages, as well as a list of all shared tasks, can be found on the CoNLL website.