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 with no code
Neural Sequence-to-Sequence Modeling with Attention by Leveraging Deep Learning Architectures for Enhanced Contextual Understanding in Abstractive Text Summarization
A deep sequence-to-sequence (seq2seq) model with an attention mechanism is employed to predict a generalized summary based on the vector representation.
PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian Tweets
Disambiguating the meaning of such terms might help the detection of misogyny.
A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation
This paper explores techniques that focus on understanding and resolving ambiguity in language within the field of natural language processing (NLP), highlighting the complexity of linguistic phenomena such as polysemy and homonymy and their implications for computational models.
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
Supervised models for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular benchmarks.
Resolving Regular Polysemy in Named Entities
Word sense disambiguation primarily addresses the lexical ambiguity of common words based on a predefined sense inventory.
Improving Word Sense Disambiguation in Neural Machine Translation with Salient Document Context
Lexical ambiguity is a challenging and pervasive problem in machine translation (\mt).
SALMA: Arabic Sense-Annotated Corpus and WSD Benchmarks
To establish a Word Sense Disambiguation baseline using our SALMA corpus, we developed an end-to-end Word Sense Disambiguation system using Target Sense Verification.
A Survey on Semantic Processing Techniques
We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks.
Word Sense Disambiguation as a Game of Neurosymbolic Darts
The core of our methodology is a neurosymbolic sense embedding, in terms of a configuration of nested balls in n-dimensional space.
Augmenters at SemEval-2023 Task 1: Enhancing CLIP in Handling Compositionality and Ambiguity for Zero-Shot Visual WSD through Prompt Augmentation and Text-To-Image Diffusion
SD Sampling uses text-to-image Stable Diffusion to generate multiple images from the given phrase, increasing the likelihood that a subset of images match the one that paired with the text.
