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 …

People make judgments about thousands of different objects and concepts on a day-to-day
basis; however, capturing the knowledge that subserves these judgments has been difficult …

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

Distributional semantics develops theories and methods to represent the meaning of natural
language expressions, with vectors encoding their statistical distribution in linguistic …

This paper describes the SemEval 2018 Shared Task on Hypernym Discovery. We put
forward this task as a complementary benchmark for modeling hypernymy, a problem which …

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 …

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

We introduce HyperLex—a data set and evaluation resource that quantifies the extent of the
semantic category membership, that is, type-of relation, also known as hyponymy …

We consider the task of inferring is-a relationships from large text corpora. For this purpose,
we propose a new method combining hyperbolic embeddings and Hearst patterns. This …

Cross-lingual word embeddings are becoming increasingly important in multilingual NLP.
Recently, it has been shown that these embeddings can be effectively learned by aligning …