Abstract: Natural Language Processing (NLP) has seen an explosion of interest in recent years, with many industrial applications relying on key technological developments in the field. However, Natural Language Understanding (NLU) - which requires the machine to get beyond processing strings and involves a semantic level - is particularly challenging due to the pervasive ambiguity of language. In this talk I will first introduce NLP and NLU and the key issues in the field, and will then move on to present recent research in my group on multilingual NLU, including work on BabelNet, our multilingual encyclopedic dictionary, and tasks such as multilingual word sense disambiguation and semantic role labeling. The key goal we aim at is to scale across languages easily and achieve state-of-the-art performance thanks to the integration of deep learning and explicit knowledge. I will also show several demos and discuss the technological transfer to Babelscape, a successful Sapienza startup company which brings to the market innovative tools for multilingual concept and entity extraction, enterprise knowledge graph creation and multilingual semantic search.