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
Latest papers
Together We Make Sense -- Learning Meta-Sense Embeddings from Pretrained Static Sense Embeddings
livnlp/npms
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Our proposed method can combine source sense embeddings that cover different sets of word senses.
Adversarial Multi-task Learning for End-to-end Metaphor Detection
silasthu/admul
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We leverage adversarial training to align the data distributions of MD and BSD in the same feature space, so task-invariant representations can be learned.
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
kaist-lklab/cot-collection
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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.
Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation
ryanliut/wsd-ue
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Word sense disambiguation (WSD), which aims to determine an appropriate sense for a target word given its context, is crucial for natural language understanding.
Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement
A4Bio/OpenCPD
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After witnessing the great success of pretrained models on diverse protein-related tasks and the fact that recovery is highly correlated with confidence, we wonder whether this knowledge can push the limits of protein design further.
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling
Most existing topic models rely on bag-of-words (BOW) representation, which limits their ability to capture word order information and leads to challenges with out-of-vocabulary (OOV) words in new documents.
Perturbation-based QE: An Explainable, Unsupervised Word-level Quality Estimation Method for Blackbox Machine Translation
Quality Estimation (QE) is the task of predicting the quality of Machine Translation (MT) system output, without using any gold-standard translation references.
Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation
The issue of word sense ambiguity poses a significant challenge in natural language processing due to the scarcity of annotated data to feed machine learning models to face the challenge.
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information
soon91jae/uvwsd
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Specifically, we suggest employing Bayesian inference to incorporate the sense definitions when sense information of the answer is not provided.
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions
The results demonstrate that our proposed LaMini-LM models are comparable to competitive baselines, while being much smaller in size.
