Transformer-based language models have taken many fields in NLP by storm. BERT and its
derivatives dominate most of the existing evaluation benchmarks, including those for Word …

Vector-based word representation paradigms situate lexical meaning at different levels of
abstraction. Distributional and static embedding models generate a single vector per word …

Transformer-based language models have taken many fields in NLP by storm. BERT and its
derivatives dominate most of the existing evaluation benchmarks, including those for Word …

Recent work has shown success in learning word embeddings with neural network
language models (NNLM). However, the majority of previous NNLMs represent each word …

Word sense disambiguation and the related field of automated word sense induction
traditionally assume that the occurrences of a lemma can be partitioned into senses. But this …

Word Sense Disambiguation (WSD), the process of automatically identifying the meaning of
a polysemous word in a sentence, is a fundamental task in Natural Language Processing …

The study of homonymy is vital to resolving fundamental problems in lexical semantics. In
this paper, we propose four hypotheses that characterize the unique behavior of homonyms …

A common way of describing the senses of ambiguous words in multilingual Word Sense
Disambiguation (WSD) is by reference to their translation equivalents in another language …

In this paper, we present a new method based on co-occurrence graphs for performing
Cross-Lingual Word Sense Disambiguation (CLWSD). The proposed approach comprises …

Unsupervised sense induction methods offer a solution to the problem of scarcity of
semantic resources. These methods automatically extract semantic information from textual …