Papers Word Sense Disambiguation
“Word Sense Disambiguation” 태그가 달린 논문 1,056편 · 필터 해제
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 DisambiguationIn the LLM era, Word Sense Induction remains unsolved
In the absence of sense-annotated data, word sense induction (WSI) is a compelling alternative to word sense disambiguation, particularly in low-resource or domain-specific settings. In this paper, we emphasize methodolo…
Word Sense DisambiguationWord Sense InductionData AugmentationAn Exploration-Analysis-Disambiguation Reasoning Framework for Word Sense Disambiguation with Low-Parameter LLMs
Word Sense Disambiguation (WSD) remains a key challenge in Natural Language Processing (NLP), especially when dealing with rare or domain-specific senses that are often misinterpreted. While modern high-parameter Large L…
Word Sense DisambiguationVisual Word Sense Disambiguation with CLIP through Dual-Channel Text Prompting and Image Augmentations
Ambiguity poses persistent challenges in natural language understanding for large language models (LLMs). To better understand how lexical ambiguity can be resolved through the visual domain, we develop an interpretable …
Natural Language UnderstandingWord Sense DisambiguationImage AugmentationBridging Lexical Ambiguity and Vision: A Mini Review on Visual Word Sense Disambiguation
This paper offers a mini review of Visual Word Sense Disambiguation (VWSD), which is a multimodal extension of traditional Word Sense Disambiguation (WSD). VWSD helps tackle lexical ambiguity in vision-language tasks. Wh…
Word Sense DisambiguationText-to-Image GenerationPrompt EngineeringCreating a Hybrid Rule and Neural Network Based Semantic Tagger using Silver Standard Data: the PyMUSAS framework for Multilingual Semantic Annotation
Word Sense Disambiguation (WSD) has been widely evaluated using the semantic frameworks of WordNet, BabelNet, and the Oxford Dictionary of English. However, for the UCREL Semantic Analysis System (USAS) framework, no ope…
Word Sense DisambiguationQuantum Visual Word Sense Disambiguation: Unraveling Ambiguities Through Quantum Inference Model
Visual word sense disambiguation focuses on polysemous words, where candidate images can be easily confused. Traditional methods use classical probability to calculate the likelihood of an image matching each gloss of th…
Word Sense DisambiguationQuantum Machine LearningImage MatchingStart Making Sense(s): A Developmental Probe of Attention Specialization Using Lexical Ambiguity
Despite an in-principle understanding of self-attention matrix operations in Transformer language models (LMs), it remains unclear precisely how these operations map onto interpretable computations or functions--and how …
Word Sense DisambiguationIntegrating Symbolic Natural Language Understanding and Language Models for Word Sense Disambiguation
Word sense disambiguation is a fundamental challenge in natural language understanding. Current methods are primarily aimed at coarse-grained representations (e.g. WordNet synsets or FrameNet frames) and require hand-ann…
Natural Language UnderstandingWord Sense DisambiguationViConBERT: Context-Gloss Aligned Vietnamese Word Embedding for Polysemous and Sense-Aware Representations
Recent advances in contextualized word embeddings have greatly improved semantic tasks such as Word Sense Disambiguation (WSD) and contextual similarity, but most progress has been limited to high-resource languages like…
Word Sense DisambiguationContrastive LearningAdverbs Revisited: Enhancing WordNet Coverage of Adverbs with a Supersense Taxonomy
WordNet offers rich supersense hierarchies for nouns and verbs, yet adverbs remain underdeveloped, lacking a systematic semantic classification. We introduce a linguistically grounded supersense typology for adverbs, emp…
Word Sense DisambiguationSentiment AnalysisEvent ExtractionLANE: Lexical Adversarial Negative Examples for Word Sense Disambiguation
Fine-grained word meaning resolution remains a critical challenge for neural language models (NLMs) as they often overfit to global sentence representations, failing to capture local semantic details. We propose a novel …
Word Sense DisambiguationRepresentation LearningContrastive LearningChange Detection\textsc{CantoNLU}: A benchmark for Cantonese natural language understanding
Cantonese, although spoken by millions, remains under-resourced due to policy and diglossia. To address this scarcity of evaluation frameworks for Cantonese, we introduce \textsc{\textbf{CantoNLU}}, a benchmark for Canto…
Natural Language UnderstandingNatural Language InferenceWord Sense DisambiguationLinguistic AcceptabilityCOLE: a Comprehensive Benchmark for French Language Understanding Evaluation
To address the need for a more comprehensive evaluation of French Natural Language Understanding (NLU), we introduce COLE, a new benchmark composed of 23 diverse task covering a broad range of NLU capabilities, including…
Natural Language UnderstandingWord Sense DisambiguationSentiment AnalysisLanguage ModellingPrompt Balance Matters: Understanding How Imbalanced Few-Shot Learning Affects Multilingual Sense Disambiguation in LLMs
Recent advances in Large Language Models (LLMs) have significantly reshaped the landscape of Natural Language Processing (NLP). Among the various prompting techniques, few-shot prompting has gained considerable attention…
Word Sense DisambiguationFew-Shot Learning