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Word Embeddings

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 51–100 of 4002 papers

TitleStatusHype
Improving Bilingual Lexicon Induction with Cross-Encoder RerankingCode1
Using Context-to-Vector with Graph Retrofitting to Improve Word EmbeddingsCode1
MorphTE: Injecting Morphology in Tensorized EmbeddingsCode1
Discovering Differences in the Representation of People using Contextualized Semantic AxesCode1
Homophone Reveals the Truth: A Reality Check for Speech2VecCode1
Learning Distinct and Representative Styles for Image CaptioningCode1
LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse DictionaryCode1
Zero-shot object goal visual navigationCode1
Entity Resolution with Hierarchical Graph Attention NetworksCode1
Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word EmbeddingsCode1
Disentangling Visual Embeddings for Attributes and ObjectsCode1
Recovering Private Text in Federated Learning of Language ModelsCode1
IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic RepresentationsCode1
Word Tour: One-dimensional Word Embeddings via the Traveling Salesman ProblemCode1
Hyperbolic Relevance Matching for Neural Keyphrase ExtractionCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little CostCode1
Learning Bias-reduced Word Embeddings Using Dictionary DefinitionsCode1
Emotion-Aware Transformer Encoder for Empathetic Dialogue GenerationCode1
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for TopicsCode1
Towards Better Chinese-centric Neural Machine Translation for Low-resource LanguagesCode1
"This is my unicorn, Fluffy": Personalizing frozen vision-language representationsCode1
VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot LearningCode1
Improving Word Translation via Two-Stage Contrastive LearningCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little CostCode1
Semi-constraint Optimal Transport for Entity Alignment with Dangling CasesCode1
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR ErrorsCode1
Word Embeddings for Automatic Equalization in Audio MixingCode1
HuSpaCy: an industrial-strength Hungarian natural language processing toolkitCode1
Simple, Interpretable and Stable Method for Detecting Words with Usage Change across CorporaCode1
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language modelsCode1
Open-CyKG: An Open Cyber Threat Intelligence Knowledge GraphCode1
Keyword Assisted Embedded Topic ModelCode1
Improving Word Translation via Two-Stage Contrastive LearningCode1
Zero-Shot Learning in Named-Entity Recognition with External KnowledgeCode1
On the Impact of Temporal Representations on Metaphor DetectionCode1
discopy: A Neural System for Shallow Discourse ParsingCode1
Tracing Origins: Coreference-aware Machine Reading ComprehensionCode1
Phonetic Word EmbeddingsCode1
Conditional probing: measuring usable information beyond a baselineCode1
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language ModelsCode1
Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous GraphCode1
Apples to Apples: A Systematic Evaluation of Topic ModelsCode1
IsoScore: Measuring the Uniformity of Embedding Space UtilizationCode1
Transferring Knowledge Distillation for Multilingual Social Event DetectionCode1
NPVec1: Word Embeddings for Nepali - Construction and EvaluationCode1
Rethinking Stealthiness of Backdoor Attack against NLP ModelsCode1
A Source-Criticism Debiasing Method for GloVe EmbeddingsCode1
Improving Entity Linking through Semantic Reinforced Entity EmbeddingsCode1
Obtaining Better Static Word Embeddings Using Contextual Embedding ModelsCode1
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon InductionCode1
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