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With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation
Contextualized word embeddings have been employed effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information.
Ranked #11 on
Word Sense Disambiguation
on Supervised:
CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages
Knowing the Most Frequent Sense (MFS) of a word has been proved to help Word Sense Disambiguation (WSD) models significantly.
Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains
This limits the range of action of deep-learning approaches, which today are at the base of any NLP task and are hungry for data.
Just ``OneSeC'' for Producing Multilingual Sense-Annotated Data
The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages.
