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 …

Abstract Machines have achieved a broad and growing set of linguistic competencies,
thanks to recent progress in Natural Language Processing (NLP). Psychologists have …

Detecting hypernymy relations is a key task in NLP, which is addressed in the literature
using two complementary approaches. Distributional methods, whose supervised variants …

Distributional representations of words have been recently used in supervised settings for
recognizing lexical inference relations between word pairs, such as hypernymy and …

Contextualized word representations have become a driving force in NLP, motivating
widespread interest in understanding their capabilities and the mechanisms by which they …

Recent work on word embeddings has shown that simple vector subtraction over pre-trained
embeddings is surprisingly effective at capturing different lexical relations, despite lacking …

This paper describes the second edition of the shared task on Taxonomy Extraction
Evaluation organised as part of SemEval 2016. This task aims to extract hypernym-hyponym …

We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that
transforms any input word vector space to emphasise the asymmetric relation of lexical …

Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for
many web applications. For example, online retailers (eg, Amazon and eBay) use …

We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy
detection and directionality. While previous embeddings have shown limitations on …