Word Sense Disambiguation
16개 벤치마크 · 논문 1,056편 · 이 태스크의 논문 보기 →
Benchmarks
Words in Context
Supervised:
RUSSE
SemEval 2013 Task 12
SensEval 2
SensEval 3 Task 1
SemEval 2007 Task 7
SemEval 2007 Task 17
FEWS
WiC-TSV
BIG-bench (Anachronisms)
Knowledge-based:
SemEval 2015 Task 13
TS50
Most implemented
Language Models are Few-Shot Learners
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
FlauBERT: Unsupervised Language Model Pre-training for French
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
An Incremental Parser for Abstract Meaning Representation
Papers
PhenoNEST: A Neuro-Symbolic Framework for Ontology-Aware Multimodal Plant Phenotyping and Trait Discovery
High-throughput plant phenotyping generates valuable data that often remains trapped in unstructured text and isolated RGB images. To bridge this semantic gap, we propose a framework for constructing a multimodal granula…
Word Sense DisambiguationThe BD-LSC Dataset: Facilitating the Benchmarking of Models for Lexical Semantic Change Detection in Slang and Standard Usage
Automatic semantic change detection aims to identify how word meanings shift over time, offering insights into both linguistic and societal change. Despite recent progress in computational lexical semantic change (LSC), …
Word Sense DisambiguationChange DetectionA new semantically annotated corpus with syntactic-semantic and cross-lingual senses
We describe a new sense-tagged corpus for word sense disambiguation. The corpus is constituted of instances of 20 French polysemous verbs. Each verb instance is annotated with three sense labels: (1) the actual translati…
Word Sense DisambiguationSwanNLP at SemEval-2026 Task 5: An LLM-based Framework for Plausibility Scoring in Narrative Word Sense Disambiguation
Recent advances in language models have substantially improved Natural Language Understanding (NLU). Although widely used benchmarks suggest that Large Language Models (LLMs) can effectively disambiguate, their practical…
Natural Language UnderstandingWord Sense DisambiguationPolysemanticity or Polysemy? Lexical Identity Confounds Superposition Metrics
If the same neuron activates for both "lender" and "riverside," standard metrics attribute the overlap to superposition--the neuron must be compressing two unrelated concepts. This work explores how much of the overlap i…
Word Sense DisambiguationUkrainian Visual Word Sense Disambiguation Benchmark
This study presents a benchmark for evaluating the Visual Word Sense Disambiguation (Visual-WSD) task in Ukrainian. The main goal of the Visual-WSD task is to identify, with minimal contextual information, the most appro…
Word Sense Disambiguation