natural-language-processing-6
Word Sense Disambiguation
3260 papers • 126 benchmarks • 313 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).
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Benchmarks
These leaderboards are used to track progress in word-sense-disambiguation-6
Trend
Dataset
Best Model
Actions
Supervised:
Supervised:
SemEval 2013 Task 12
SemEval 2013 Task 12
SensEval 2
Libraries
iUse these libraries to find word-sense-disambiguation-6 models and implementations
4 papers 91
Datasets
Subtasks
Most implemented papers
Language Models are Few-Shot Learners
I. Sutskever, Tom B. Brown, Alec Radford, Christopher Berner, Christopher Hesse, Scott Gray, Dario Amodei, A. Ramesh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Jack Clark, Gretchen Krueger, T. Henighan, Benjamin Mann, Sam McCandlish, Prafulla Dhariwal, Jeff Wu, Daniel M. Ziegler, Arvind Neelakantan, Mark Chen, Nick Ryder, Clemens Winter, Ariel Herbert-Voss, Melanie Subbiah, J. Kaplan, Pranav Shyam, R. Child, Eric Sigler, Ma-teusz Litwin, Benjamin Chess•Wed May 27 2020
GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.
