Papers Word Sense Induction
“Word Sense Induction” 태그가 달린 논문 108편 · 필터 해제
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+2Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications
Scholarly text is often laden with jargon, or specialized language that can facilitate efficient in-group communication within fields but hinder understanding for out-groups. In this work, we develop and validate an inte…
Word Sense InductionWord Sense Induction with Hierarchical Clustering and Mutual Information Maximization
Word sense induction (WSI) is a difficult problem in natural language processing that involves the unsupervised automatic detection of a word's senses (i.e. meanings). Recent work achieves significant results on the WSI …
ClusteringLanguage ModelingLanguage ModellingWord Sense InductionRuDSI: graph-based word sense induction dataset for Russian
We present RuDSI, a new benchmark for word sense induction (WSI) in Russian. The dataset was created using manual annotation and semi-automatic clustering of Word Usage Graphs (WUGs). Unlike prior WSI datasets for Russia…
ClusteringGraph ClusteringWord Sense InductionAbsinth: A small world approach to word sense induction
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution
Lexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone of various NLP applications, inclu…
Data AugmentationRelation ExtractionWord Sense InductionTowards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings
Wordnets are indispensable tools for various natural language processing applications. Unfortunately, wordnets get outdated, and producing or updating wordnets can be slow and costly in terms of time and resources. This …
Language ModelingLanguage ModellingSentenceSentence Embeddings+2Topological Data Analysis for Word Sense Disambiguation
We develop and test a novel unsupervised algorithm for word sense induction and disambiguation which uses topological data analysis. Typical approaches to the problem involve clustering, based on simple low level feature…
ClusteringTopological Data AnalysisWord EmbeddingsWord Sense Disambiguation+1Word Sense Induction with Attentive Context Clustering
In this paper, we present ACCWSI (Attentive Context Clustering WSI), a method for Word Sense Induction, suitable for languages with limited resources. Pretrained on a small corpus and given an ambiguous word (query word)…
ClusteringWord Sense InductionLarge Scale Substitution-based Word Sense Induction
We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a co…
Outlier DetectionWord EmbeddingsWord Sense InductionPolyLM: Learning about Polysemy through Language Modeling
To avoid the "meaning conflation deficiency" of word embeddings, a number of models have aimed to embed individual word senses. These methods at one time performed well on tasks such as word sense induction (WSI), but th…
Language ModelingLanguage ModellingWord EmbeddingsWord Sense InductionBOS at SemEval-2020 Task 1: Word Sense Induction via Lexical Substitution for Lexical Semantic Change Detection
SemEval-2020 Task 1 is devoted to detection of changes in word meaning over time. The first subtask raises a question if a particular word has acquired or lost any of its senses during the given time period. The second s…
Change DetectionClusteringWord Sense InductionTopology of Word Embeddings: Singularities Reflect Polysemy
The manifold hypothesis suggests that word vectors live on a submanifold within their ambient vector space. We argue that we should, more accurately, expect them to live on a pinched manifold: a singular quotient of a ma…
Word EmbeddingsWord Sense InductionAn Evaluation Method for Diachronic Word Sense Induction
The task of Diachronic Word Sense Induction (DWSI) aims to identify the meaning of words from their context, taking the temporal dimension into account. In this paper we propose an evaluation method based on large-scale …
Word Sense InductionHow does BERT capture semantics? A closer look at polysemous words
The recent paradigm shift to contextual word embeddings has seen tremendous success across a wide range of down-stream tasks. However, little is known on how the emergent relation of context and semantics manifests geome…
Semanticity predictionSemantic SimilarityWord EmbeddingsWord Sense Disambiguation+2