Mathematical models of natural language can be organised into logical and statistical. The former are based on grammatical structures of phrases and sentences and the latter on distributions of words in corpora of text. In joint work with Clark (Cambridge) and Coecke (Oxford), we developed a unifying framework where the distributions of words are composed to form distributions for phrases and sentences. This expanded the application domains of the statistical models -- e.g. automatic reasoning about similarity -- from words to phrases and sentences. On the theoretical side, our model extends the word-based setting from vectors to tensors. Tensors are main players in the mathematical models of quantum mechanics. In this talk, I will review the theory and applications of our model in simple terms and through examples. I will briefly explain how `entanglement', a concept arising from tensors in quantum mechanics, manifests itself and is used as a resource in the linguistic applications. I will also explain how the reasoning toolkit used in this model is the same as that of Abramsky and Coecke in their categorical Quantum Mechanics model.