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 …

The quality of word embeddings depends on the input corpora, model architectures, and
hyper-parameter settings. Using the state-of-the-art neural embedding tool word2vec and …

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 …

We provide the first extensive evaluation of how using different types of context to learn skip-
gram word embeddings affects performance on a wide range of intrinsic and extrinsic NLP …

In this paper, we introduce EVALution 1.0, a dataset designed for the training and the
evaluation of Distributional Semantic Models (DSMs). This version consists of almost 7.5 K …

For the purpose of post-marketing drug safety surveillance, which has traditionally relied on
the voluntary reporting of individual cases of adverse drug events (ADEs), other sources of …

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 paper gives an overview of the Russian Semantic Similarity Evaluation (RUSSE) shared
task held in conjunction with the Dialogue 2015 conference. There exist a lot of comparative …

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 …