Context-predicting models (more commonly known as embeddings or neural language
models) are the new kids on the distributional semantics block. Despite the buzz …

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 …

Contextualized word embeddings, ie vector representations for words in context, are
naturally seen as an extension of previous noncontextual distributional semantic models. In …

This paper presents the results of a large-scale evaluation study of window-based
Distributional Semantic Models on a wide variety of tasks. Our study combines a broad …

In recent years, distributional models (DMs) have shown great success in representing
lexical semantics. In this work we show that the extent to which DMs represent semantic …

Most distributional lexico-semantic models derive their representations based on external
language resources such as text corpora. In this study, we propose that internal language …

Distributional semantic methods to approximate word meaning with context vectors have
been very successful empirically, and the last years have seen a surge of interest in their …

Data-driven representation learning for words is a technique of central importance in NLP.
While indisputably useful as a source of features in downstream tasks, such vectors tend to …

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 …

In this paper, we introduce an approach to automatically map a standard distributional
semantic space onto a set-theoretic model. We predict that there is a functional relationship …