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 …

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 …

Hypernymy detection, aka, lexical entailment, is a fundamental sub-task of many natural
language understanding tasks. Previous explorations mostly focus on monolingual …

Grounded in cognitive linguistics, graded lexical entailment (GR-LE) is concerned with
finegrained assertions regarding the directional hierarchical relationships between concepts …

Automatic taxonomy induction is a challenging task in the field of natural language
understanding (NLU) and information retrieval (IR) because it requires machine learning …

Lexical entailment (LE; also known as hyponymy-hypernymy or is-a relation) is a core
asymmetric lexical relation that supports tasks like taxonomy induction and text generation …

Hypernymy is a semantic relation, expressing the “is-a” relation between a concept and its
instances. Such relations are building blocks for large-scale taxonomies, ontologies and …

We investigate the hypothesis that translations can be used to identify cross-lingual lexical
entailment. We propose novel methods that leverage parallel corpora, word embeddings …

Lexical entailment (LE) is a fundamental asymmetric lexico-semantic relation, supporting the
hierarchies in lexical resources (eg, WordNet, ConceptNet) and applications like natural …

We address hypernymy detection, ie, whether an is-a relationship exists between words (x,
y), with the help of large textual corpora. Most conventional approaches to this task have …