present work on using bulk, structured, linguistic annotations in order to perform unsupervised induction of meaning for three kinds of linguistic forms: words, sentences, and documents. The primary linguistic annotation I consider throughout are frames, which encode core linguistic, background or societal knowledge necessary to understand abstract concepts and real-world situations. I will discuss how these bulk annotations can be used to better encode linguistic- and cognitive science-backed semantic expectations within word forms, learn large lexicalized and refined syntactic fragments, present scalable methods to learn high-level representations for document and discourse understanding.
Frank Ferraro is a Ph.D. candidate in Computer Science at Johns Hopkins University. His research focuses on computational event semantics, and unlabeled, structured probabilistic modeling over very large corpora. He has published basic and applied research on a number of cross-disciplinary projects, and has papers in areas such as multimodal processing and information extraction, latent-variable syntactic methods and applications, and the induction and evaluation of frames and scripts. He worked as a research intern at Microsoft Research (2015), and he was a National Science Foundation Graduate Research Fellow. He will be joining the UMBC CS department as an assistant professor.