Word Sense Disambiguation
The task of Word Sense Disambiguation (WSD) consists of associating words in context with their most suitable entry in a pre-defined sense inventory. The de-facto sense inventory for English in WSD is WordNet. For example, given the word “mouse” and the following sentence:
“A mouse consists of an object held in one's hand, with one or more buttons.”
we would assign “mouse” with its electronic device sense (the 4th sense in the WordNet sense inventory).
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Greatest papers with code
Incorporating Glosses into Neural Word Sense Disambiguation
ACL 2018
• jimiyulu/WSD_MemNN
• 
GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods.
#2 best model for
Word Sense Disambiguation
on SemEval 2015 Task 13
An Incremental Parser for Abstract Meaning Representation
EACL 2017
• mdtux89/amr-eager
• 
We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time.
NAMED ENTITY RECOGNITION (NER) SEMANTIC ROLE LABELING WORD SENSE DISAMBIGUATION
An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
The sparse mode uses the traditional vector space model to estimate the most similar word sense corresponding to its context.
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images.