EECS Special Seminar: Knowledge from Language via Deep Understanding

Almost all of human knowledge is now available online, but the vast majority of it is principally encoded in the form of human language explanations. In this talk, I explore novel neural network approaches that open up opportunities for getting a deep understanding of natural language text. First, I show how distributed representations enabled the building of a smaller, faster and more accurate dependency parser for finding the structure of sentences. Then I show how related neural technologies can be used to improve the construction of knowledge bases from text. However, maybe we don't need this intermediate step and can directly gain knowledge and answer people's questions from large textbases? In the third part, I explore this possibility by directly reading text with a simple yet highly effective neural architecture for question answering.