Despite the number of NLP studies dedicated to thematic fit estimation, little attention has
been paid to the related task of composing and updating verb argument expectations. The …

In the NLP literature, the thematic fit estimation task is defined as the task in which a system
has to predict how likely a candidate argument (eg cop) is to fit a given a verb-specific role …

The aim of this paper is to present a computational model of the dynamic composition and
update of verb argument expectations using Distributional Memory, a state-of-the-art …

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 …

Current computational models of argument constructions typically represent their semantic
content with hand-made formal structures. Here we present a distributional model …

This work addresses some questions about language processing: what does it mean that
natural language sentences are semantically complex? What semantic features can …

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 …

Representing predicates in terms of their argument distribution is common practice in NLP.
Multi-word predicates (MWPs) in this context are often either disregarded or considered as …

How quickly can verb-argument relations be computed to impact predictions of a
subsequent argument? We take advantage of the substantial differences in verb-argument …

In lexicalist linguistic theories, argument structure is assumed to be predictable from the
meaning of verbs. As a result, the verb is the primary determinant of the meaning of a clause …