Word Sense Disambiguation Edit Task
Natural Language Processing
About
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).
Benchmarks
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Subtasks
Datasets
Greatest papers with code
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
ICLR 2021
• huggingface/transformers
• 
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
Ranked #1 on
Question Answering
on SQuAD1.1 dev
COMMON SENSE REASONING COREFERENCE RESOLUTION LINGUISTIC ACCEPTABILITY NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING READING COMPREHENSION SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS WORD SENSE DISAMBIGUATION
FlauBERT: Unsupervised Language Model Pre-training for French
LREC 2020
• huggingface/transformers
•
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE TEXT CLASSIFICATION WORD SENSE DISAMBIGUATION
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Ranked #1 on
Language Modelling
on LAMBADA
COMMON SENSE REASONING COREFERENCE RESOLUTION DOMAIN ADAPTATION FEW-SHOT LEARNING LANGUAGE MODELLING MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SENTENCE COMPLETION UNSUPERVISED MACHINE TRANSLATION WORD SENSE DISAMBIGUATION
Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures
EMNLP 2018
• awslabs/sockeye
• 
Recently, non-recurrent architectures (convolutional, self-attentional) have outperformed RNNs in neural machine translation.
Distributed Word Representation in Tsetlin Machine
This restriction has constrained the performance of TM compared to deep neural networks (DNNs) in NLP.
SENTIMENT ANALYSIS TEXT CLASSIFICATION WORD SENSE DISAMBIGUATION
Knowledge Enhanced Contextual Word Representations
Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities.
Ranked #6 on
Relation Extraction
on TACRED
(using extra training data)
ENTITY LINKING ENTITY TYPING LANGUAGE MODELLING RELATION EXTRACTION WORD SENSE DISAMBIGUATION
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 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
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
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.
Ranked #3 on
Word Sense Disambiguation
on SemEval 2015 Task 13