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
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Greatest papers with code
Improved Word Representation Learning with Sememes
The key idea is to utilize word sememes to capture exact meanings of a word within specific contexts accurately.
COMMON SENSE REASONING LANGUAGE MODELLING MACHINE TRANSLATION SENTIMENT ANALYSIS WORD EMBEDDINGS WORD SENSE DISAMBIGUATION WORD SENSE INDUCTION
Dict2vec : Learning Word Embeddings using Lexical Dictionaries
Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks.
KNOWLEDGE GRAPHS LEARNING WORD EMBEDDINGS MACHINE TRANSLATION READING COMPREHENSION SEMANTIC ROLE LABELING TEXT CLASSIFICATION WORD SENSE DISAMBIGUATION
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 SEMANTIC ROLE LABELING WORD SENSE DISAMBIGUATION
Cross-lingual Lexical Sememe Prediction
We propose a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction.
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.
SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation
In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD).
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.
Learning Graph Embeddings from WordNet-based Similarity Measures
SEMEVAL 2019 • uhh-lt/path2vec
We present path2vec, a new approach for learning graph embeddings that relies on structural measures of pairwise node similarities.
GRAPH EMBEDDING SEMANTIC TEXTUAL SIMILARITY WORD SENSE DISAMBIGUATION