CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning

Topics
We invite the submission of papers on all aspects of computational approaches to natural language learning, including, but not limited to:
Development and empirical evaluation of machine learning methods applied to any natural language or speech processing task in supervised, semi-supervised or unsupervised settings (e.g. structured prediction, graphical models, deep learning, relational learning, reinforcement learning, etc.).
Theoretical analyses of learning-based approaches to natural language processing.
Computational models of human language acquisition and processing, models of language evolution and change, and simulation and analysis of psycholinguistic findings.
Special Topic
With the recent growing interest in statistical approaches to natural language learning that go beyond linear models and convex optimization, e.g. latent variable models or deep learning approaches, including recurrent and recursive networks, we are especially interested in papers dealing with theoretical or empirical analyses and of such models and their learning algorithms. This includes analysis of non-convex learning in the context of language processing, as well as theoretical or empirical results on phenomena that can and cannot be learned well with a given approach.