Abstract

Traditional semantic representation frameworks generally define complex, often exclusive category systems that require highly trained annotators to build. And in spite of their high quality for the cases they are designed to handle, these frameworks can be brittle to cases that (i) deviate from prototypical instances of a category; (ii) are equally good instances of multiple categories; or (iii) fall under a category that was erroneously excluded from the framework’s ontology.

I present an alternative approach to semantic representation that addresses these issues, under the auspices of the Decompositional Semantics Initiative. In this approach, which is rooted in a long tradition of theoretical approaches to lexical semantics, semantic representations are decomposed into a straightforwardly extensible set of core inferences about entities and events that any native speaker can recognize—e.g. inferences about the animacy of an entity referred to by a noun phrase or the the factuality and temporal duration of an events referred to by a predicate. A consequence of this approach is that semantic annotation can take the form of many simple questions about words or phrases (in context) that are easy for naive native speakers to answer, thus allowing annotations to be crowd-sourced while retaining high interannotator agreement.

I discuss five datasets that apply this decompositional approach to five domains—semantic roles, event factuality, genericity, temporal relations, and entity typing—along with a range of models aimed at predicting decomposed representations of these phenomena. I then present a recently released graph bank—Universal Decompositional Semantics v1.0 (UDS1.0)—that unifies these datasets into a single semantic graph representation, annotated with real-valued node and edge attributes, as well as a parser that can predict these graphs and their annotations from raw text.

Biography

Aaron is an Assistant Professor in the Department of Linguistics at the University of Rochester, with secondary appointments in the Department of Computer Science and the Department of Brain and Cognitive Sciences and an affiliation with the Goergen Institute for Data Science. He is also the Director of the FACTS.lab at UR, and co-leads two multi-institution projects: the MegaAttitude Project and the Decompositional Semantic Initiative. Before joining the University of Rochester, he received his PhD in Linguistics from the University of Maryland in 2015 and was a postdoctoral fellow at Johns Hopkins University’s Science of Learning Institute with affiliations in the Department of Cognitive Science and the Center for Language and Speech Processing from 2015 to 2017. Aaron’s research focuses on issues of semantic representation and natural language ontology, both in humans and machines. It aims to understand how humans represent the meanings of words, how those meanings relate to the meanings of the syntactic structures those words occur in, and how the nature of these representations can inform the way natural language understanding systems are built.