Do Supervised Distributional Methods Really Learn Lexical Inference Relations?

Integrating Multiplicative Features into Supervised Distributional Methods for Lexical Entailment

Supervised distributional methods are applied successfully in lexical entailment, but recent work questioned whether these methods actually learn a relation between two words. Specifically, Levy et al. (2015) claimed tha…

Which distributional cues help the most? Unsupervised contexts selection for lexical category acquisition

Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation

We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional seman…

ReFRAME or Remain: Unsupervised Lexical Semantic Change Detection with Frame Semantics

The majority of contemporary computational methods for lexical semantic change (LSC) detection are based on neural embedding distributional representations. Although these models perform well on LSC benchmarks, their res…

Scoring Lexical Entailment with a Supervised Directional Similarity Network

We present the Supervised Directional Similarity Network (SDSN), a novel neural architecture for learning task-specific transformation functions on top of general-purpose word embeddings. Relying on only a limited amount…