… either directly learn sense representations from corpora or rely on sense inventories from …
sense inventory from existing word embeddings via clustering of ego-networks of related words…

… improvements upon integrating word embeddings, including … properties of words, the
effectiveness of word embeddings is … One representation per word - does it make sense for …

… In this paper, we tested the semantic properties of contextualized word embeddings (CWEs)
to address word sense disambiguation.To test their capabilities to distinguish different …

… Once created, these word vectors, called word embeddings since each word is embedded
by the learning process in an n-dimensional space, are used as a replacement for the words …

… word embeddings of size 200 trained for 3 epochs on Librusec and containing only … word
embedding models, clustering algorithms and word weighting methods for the context words…

… -network clustering, and aggregates word vectors with their possible word-sense vectors.
In contrast with these approaches, we use only single vector word embeddings to support our …

… We use fastText to generate static word embeddings for the lemmas ( vl… word embeddings
to our previous embeddings. When making predictions, we also compute fastText embeddings …

… links word sense embeddings to a lexical resource, making … While most word embedding
approaches represent a term … approaches to produce word sense embeddings from corpora (…

… senses, existing systems are still very limited in the sense that they make use of structured, …
in word embeddings research to generate context embeddings, which are embeddings …

… we study how word embeddings can be used in Word Sense … methods through which word
embeddings can be leveraged … a WSD system that makes use of word embeddings alone, if …