Distributional semantics is a usage-based model of meaning, based on the assumption that
the statistical distribution of linguistic items in context plays a key role in characterizing their …

Distributional semantics has deeply changed in the last decades. First, predict models stole
the thunder from traditional count ones, and more recently both of them were replaced in …

Nominal compounds such as red wine and nut case display a continuum of compositionality,
with varying contributions from the components of the compound to its semantics. This article …

The distributional pattern of words in language forms the basis of linguistic distributional
knowledge and contributes to conceptual processing, yet many questions remain regarding …

Attempts to find a single technique for general-purpose intrinsic evaluation of word
embeddings have so far not been successful. We present a new approach based on scaled …

Neural word embeddings models (such as those built with word2vec) are known to have
stability problems: when retraining a model with the exact same hyperparameters, words …

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

We evaluate four count-based and predictive distributional semantic models of Ancient
Greek against AGREE, a composite benchmark of human judgements, to assess their ability …

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 this paper, we provide an overview of research on multiword expressions (MWEs), from a
natural language processing perspective. We examine methods developed for modelling …