Goergen Institute for Data Science Speaker
Abstract: Knowing a language's words is a fundamental component of knowing that language. A major part of knowing a word is knowing (i) what it means and (ii) how it distributes. A long-standing question in the linguistics literature is how these two kinds of knowledge are related. In this talk, I discuss two lines of work focused on how the meaning-distribution relationship is instantiated in the domain of verbsóin particular, how verbs' semantic arguments are mapped to their syntactic arguments. Taking a cue from recent work in computational semantics, I show how this question can be approached as a problem of multiview factorization under theoretically motivated constraints.
In the first part of the talk, I focus on the problem of determining the mapping from semantic arguments to nominals, developing a generalization of Dowty's seminal prototype-theoretic approach to semantic roles. In the second part of the talk, I turn to the problem of determining the mapping from semantic arguments to clauses, developing a model for jointly inducing (a) verbs' semantic types and (b) probabilistic rules of projection from those semantic types to syntactic types. I conclude with prospects for a unified computational model of syntactic and semantic argument-taking.
Bio: Aaron Steven White is a postdoctoral researcher in the Science of Learning Institute at Johns Hopkins University, with affiliations in the Department of Cognitive Science and the Center for Language and Speech Processing. He received his Ph.D. from the Department of Linguistics at the University of Maryland in 2015 and his B.A. in Linguistics from the University of California, Santa Cruz in 2009. His research aims to uncover the relationship between natural language syntax and semantics using large-scale behavioral and corpus data in conjunction with computational models informed by linguistic theory.
Host: Jeff Runner, firstname.lastname@example.org