Computational models for understanding natural language by machines. Machines that can read texts and understand what it is about (what, who, when, where), but also machines that create powerful distributional language models from large volume of text using Deep Learning techniques. Our research tries to obtain a better understanding of so-called backbone models, reveal biases and unwanted errors but also to combine distributional approaches with explicit symbolic models to add explanatory power.
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