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 Representation learning has become an invaluable approach for learning from
symbolic data such as text and graphs. However, state-of-the-art embedding methods …

Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical
structure that the next generation of unsupervised learned word embeddings should reveal …

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 …

Methods for unsupervised hypernym detection may broadly be categorized according to two
paradigms: pattern-based and distributional methods. In this paper, we study the …

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 …