Word Sense Disambiguation
92 papers with code • 13 benchmarks • 16 datasets
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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Datasets
SuperGLUE
Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison
Senseval-2
Verse
Greatest papers with code
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
huggingface/transformers
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ICLR 2021
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
Ranked #1 on
Natural Language Inference
on CommitmentBank
FlauBERT: Unsupervised Language Model Pre-training for French
huggingface/transformers
•
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LREC 2020
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
Ranked #1 on
Natural Language Inference
on XNLI French
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
Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures
awslabs/sockeye
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EMNLP 2018
Recently, non-recurrent architectures (convolutional, self-attentional) have outperformed RNNs in neural machine translation.
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
The approach significantly enhances the performance and interpretability of TM.
Ranked #3 on
Text Classification
on R8
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 #9 on
Relation Extraction
on SemEval-2010 Task 8
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.
NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task--Next Sentence Prediction
Using prompts to utilize language models to perform various downstream tasks, also known as prompt-based learning or prompt-learning, has lately gained significant success in comparison to the pre-train and fine-tune paradigm.
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.
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
yueyu1030/COSINE
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NAACL 2021
To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision.
Ranked #1 on
Word Sense Disambiguation
on Words in Context