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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.
Personalized PageRank with Syntagmatic Information for Multilingual Word Sense Disambiguation
Exploiting syntagmatic information is an encouraging research focus to be pursued in an effort to close the gap between knowledge-based and supervised Word Sense Disambiguation (WSD) performance.
SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations
Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed.
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