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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 176–200 of 4002 papers

TitleStatusHype
Zero-Shot Semantic SegmentationCode1
All Word Embeddings from One EmbeddingCode1
Inducing Systematicity in Transformers by Attending to Structurally Quantized EmbeddingsCode1
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited DataCode1
Interpretable & Time-Budget-Constrained Contextualization for Re-RankingCode1
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for TopicsCode1
IsoScore: Measuring the Uniformity of Embedding Space UtilizationCode1
Backpack Language ModelsCode1
BERT for Monolingual and Cross-Lingual Reverse DictionaryCode1
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense DisambiguationCode1
Language Models Implement Simple Word2Vec-style Vector ArithmeticCode1
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP ModelsCode1
Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel CorporaCode1
Learning Object-Language Alignments for Open-Vocabulary Object DetectionCode1
Learning principled bilingual mappings of word embeddings while preserving monolingual invarianceCode1
Meta-Personalizing Vision-Language Models to Find Named Instances in VideoCode1
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic SegmentationCode1
MLFMF: Data Sets for Machine Learning for Mathematical FormalizationCode1
Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion RecognitionCode1
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label GraphsCode1
Multilingual acoustic word embedding models for processing zero-resource languagesCode1
Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item AnnotationCode1
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide ImagesCode1
GLOW : Global Weighted Self-Attention Network for Web SearchCode1
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at ScaleCode1
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