Technical Proposal Algorithmic and Logical Aspects when Composing Meanings

Words are the building blocks of sentences, yet meaning of a sentence goes well beyond meanings of words therein. Indeed, while we do have dictionaries for words, we don’t seem to need them to infer the meaning of a sentence from meanings of its constituents. Discovering the process of meaning assignment in natural languages is one of the most foundational issues in linguistics and com-puter science, whose findings will assist in crafting applications to automate many language-related tasks, such as document search, automated text generation, an many others. To date, the compositional type-logical [16, 14] and the distributional proba-bilistic models [17, 9] have provided two complementary partial solutions to the problem of meaning assigning in natural languages. The logical approach is based on classical ideas from mathematical logic, mainly Frege’s principle that meaning of a sentence can be derived from the relations of the words in it. The distributional model is more recent, it can be related to Wittgenstein’s philosophy of ‘meaning as use’, whereby meanings of words can be determined from their context. The logical models have been the champions on the theory side, whereas in practice their probabilistic rivals have provided the best predictions. This two-sortedness of defining properties of meaning: ‘logical form ’ versus ‘contextual use’, has left the question of ‘what is the foundational structure of meaning? ’ even more open a question than before. A breakthrough towards this goal was achieved by the PIs of this proposal, by proposing a compositional distributional model of meaning that combines the compositional type-logical and the distributional probabilistic models (hyperlink)