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.
#3 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
Zero-shot Word Sense Disambiguation using Sense Definition Embeddings
ACL 2019
• malllabiisc/EWISE
• 
To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space.
GENERALIZED ZERO-SHOT LEARNING KNOWLEDGE GRAPH EMBEDDING WORD SENSE DISAMBIGUATION
Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation
In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton WordNet, and thus reduce the number of different sense tags that must be observed to disambiguate all words of the lexical database.
Making Fast Graph-based Algorithms with Graph Metric Embeddings
The computation of distance measures between nodes in graphs is inefficient and does not scale to large graphs.
SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation
In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD).
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.