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
HKUST at SemEval-2023 Task 1: Visual Word Sense Disambiguation with Context Augmentation and Visual Assistance
thomas-yin/semeval-2023-task1
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Visual Word Sense Disambiguation (VWSD) is a multi-modal task that aims to select, among a batch of candidate images, the one that best entails the target word's meaning within a limited context.
How Contentious Terms About People and Cultures are Used in Linked Open Data
In some cases, LOD contributors mark contentious terms with words and phrases in literals (implicit markers) or properties linked to resources (explicit markers).
Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation
We also study VWSD as a unimodal problem by converting to text-to-text and image-to-image retrieval, as well as question-answering (QA), to fully explore the capabilities of relevant models.
Can Word Sense Distribution Detect Semantic Changes of Words?
LivNLP/Sense-based-Semantic-Change-Prediction
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Given this relationship between WSD and SCD, we explore the possibility of predicting whether a target word has its meaning changed between two corpora collected at different time steps, by comparing the distributions of senses of that word in each corpora.
Language Models as Knowledge Bases for Visual Word Sense Disambiguation
anastasiakrith/llm-for-vwsd
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Visual Word Sense Disambiguation (VWSD) is a novel challenging task that lies between linguistic sense disambiguation and fine-grained multimodal retrieval.
OYXOY: A Modern NLP Test Suite for Modern Greek
This paper serves as a foundational step towards the development of a linguistically motivated and technically relevant evaluation suite for Greek NLP.
ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure
This paper presents ContrastWSD, a RoBERTa-based metaphor detection model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the contextual meaning with the basic meaning of a word to determine whether it is used metaphorically in a sentence.
Combating the Curse of Multilinguality in Cross-Lingual WSD by Aligning Sparse Contextualized Word Representations
begab/sparsity_makes_sense
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NAACL 2022
In this paper, we advocate for using large pre-trained monolingual language models in cross lingual zero-shot word sense disambiguation (WSD) coupled with a contextualized mapping mechanism.
UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation
We present a novel algorithm that leverages glosses retrieved from BabelNet, in combination with text and image encoders.
Word sense extension
jadeleiyu/word_sense_extension
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Humans often make creative use of words to express novel senses.
