Vector-based word representation paradigms situate lexical meaning at different levels of
abstraction. Distributional and static embedding models generate a single vector per word …

We present the first large-scale English “allwords lexical substitution” corpus. The size of the
corpus provides a rich resource for investigations into word meaning. We investigate the …

We introduce DISSECT, a toolkit to build and explore computational models of word, phrase
and sentence meaning based on the principles of distributional semantics. The toolkit …

This paper demonstrates how token-level Word Space Models (a distributional semantic
technique that was originally developed in statistical natural language processing) can be …

NLP tasks differ in the semantic information they require, and at this time no single semantic
representation fulfills all requirements. Logic-based representations characterize sentence …

Distributional semantic models have become a mainstay in NLP, providing useful features
for downstream tasks. However, assessing long-term progress requires explicit long-term …

This paper investigates the representation of proper names in distributional semantics. We
define three properties we expect names to display: uniqueness (being a unique entity) …

Vector space models have become popular in distributional semantics, despite the
challenges they face in capturing various semantic phenomena. We propose a novel …

Lectometry is a corpus-based methodology that explores how multiple language-external
dimensions shape language usage in an aggregate perspective. The paper combines this …

We introduce WHiC, a challenging testbed for detecting hypernymy, an asymmetric relation
between words. While previous work has focused on detecting hypernymy between word …