GoingToMeet

CoNLL is an international conference for research on natural language learning. We invite submission of papers about natural language learning topics, including, but not limited to:

* Computational models of human language acquisition
* Computational models of the evolution of language
* Machine learning methods applied to natural language processing tasks (speech processing, phonology, morphology, syntax, semantics, discourse processing, language engineering applications)
* Statistical methods (Bayesian learning, graphical models, kernel methods, statistical models for structured problems)
* 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)
* Reinforcement learning
* Active learning, ensemble methods, meta-learning
* Learning architectures for structural and relational NLP tasks
* Computational learning theory analysis of language learning
* Empirical and theoretical comparisons of language learning methods
* Models of induction and analogy in linguistics