Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …

Prior research has explored the ability of computational models to predict a word semantic fit
with a given predicate. While much work has been devoted to modeling the typicality relation …

Ellipsis is a linguistic phenomenon characterized by the omission of one or more sentence
elements. Solving such a linguistic construction is not a trivial issue in natural language …

Most compositional distributional semantic models represent sentence meaning with a
single vector. In this paper, we propose a structured distributional model (SDM) that …

Current methods for event representation ignore related events in a corpus-level global
context. For a deep and comprehensive understanding of complex events, we introduce a …

Abstract Functional Distributional Semantics is a linguistically motivated framework for
modelling lexical and sentence-level semantics with truth-conditional functions using …

Modeling thematic fit (a verb--argument compositional semantics task) currently requires a
very large burden of labeled data. We take a linguistically machine-annotated large corpus …

While neural embeddings represent a popular choice for word representation in a wide
variety of NLP tasks, their usage for thematic fit modeling has been limited, as they have …

The thematic fit estimation task measures the compatibility between a predicate (typically a
verb), an argument (typically a noun phrase), and a specific semantic role assigned to the …

While people process text, they make frequent use of information that is assumed to be
common ground and left implicit in the text. One important type of such commonsense …