Word Sense Disambiguation
142 papers with code • 15 benchmarks • 15 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
These leaderboards are used to track progress in Word Sense Disambiguation
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Libraries
Use these libraries to find Word Sense Disambiguation models and implementationsDatasets
BIG-bench
WiC
Senseval-2
Verse
Most implemented papers
Chinese Word Sense Embedding with SememeWSD and Synonym Set
2023-MindSpore-1/ms-code-196
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To address this limitation, we propose SememeWSD Synonym (SWSDS) model to assign a different vector to every sense of polysemous words with the help of word sense disambiguation (WSD) and synonym set in OpenHowNet.
N-Grammer: Augmenting Transformers with latent n-grams
Transformer models have recently emerged as one of the foundational models in natural language processing, and as a byproduct, there is significant recent interest and investment in scaling these models.
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
joeljang/elm
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Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks.
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
Furthermore, we show that instruction tuning with CoT Collection allows LMs to possess stronger few-shot learning capabilities on 4 domain-specific tasks, resulting in an improvement of +2. 24% (Flan-T5 3B) and +2. 37% (Flan-T5 11B), even outperforming ChatGPT utilizing demonstrations until the max length by a +13. 98% margin.
FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language Model
Lexical substitutes have found use in areas such as paraphrasing, text simplification, machine translation, word sense disambiguation, and part of speech induction.
