Word Sense Induction
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Benchmarks
SemEval 2010 WSI
Most implemented
Breaking Sticks and Ambiguities with Adaptive Skip-gram
RuDSI: graph-based word sense induction dataset for Russian
Towards better substitution-based word sense induction
A Simple Approach to Learn Polysemous Word Embeddings
Papers
In 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 AugmentationTo Word Senses and Beyond: Inducing Concepts with Contextualized Language Models
Polysemy and synonymy are two crucial interrelated facets of lexical ambiguity. While both phenomena are widely documented in lexical resources and have been studied extensively in NLP, leading to dedicated systems, they…
ClusteringLEMMAWord Sense InductionMultilingual Substitution-based Word Sense Induction
Word Sense Induction (WSI) is the task of discovering senses of an ambiguous word by grouping usages of this word into clusters corresponding to these senses. Many approaches were proposed to solve WSI in English and a f…
Language ModelingLanguage ModellingWord Sense InductionThe LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks
Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of …
Change DetectionLEMMAModel OptimizationWord Sense InductionA Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change
Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC). Current evaluations typically focus on a specific task known as Graded Change Detection (GCD). However, performance comparison …
Change DetectionWord EmbeddingsWord Sense InductionWord Sense Induction with Knowledge Distillation from BERT
Pre-trained contextual language models are ubiquitously employed for language understanding tasks, but are unsuitable for resource-constrained systems. Noncontextual word embeddings are an efficient alternative in these …
Knowledge DistillationLanguage ModelingLanguage ModellingWord Embeddings+2