Human similarity and relatedness judgements between concepts underlie most of cognitive
capabilities, such as categorisation, memory, decision-making and reasoning. For this …

Over the past years, distributed semantic representations have proved to be effective and
flexible keepers of prior knowledge to be integrated into downstream applications. This …

In this work, we present a novel counter-fitting method which injects antonymy and
synonymy constraints into vector space representations in order to improve the vectors' …

Unsupervised machine translation---ie, not assuming any cross-lingual supervision signal,
whether a dictionary, translations, or comparable corpora---seems impossible, but …

Integrating computer vision and natural language processing is a novel interdisciplinary field
that has received a lot of attention recently. In this survey, we provide a comprehensive …

Verbs play a critical role in the meaning of sentences, but these ubiquitous words have
received little attention in recent distributional semantics research. We introduce SimVerb …

Abstract We present Attract-Repel, an algorithm for improving the semantic quality of word
vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the …

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 Charagram embeddings, a simple approach for learning character-based
compositional models to embed textual sequences. A word or sentence is represented using …

Owing to the need for a deep understanding of linguistic items, semantic representation is
considered to be one of the fundamental components of several applications in Natural …