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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 301–325 of 4002 papers

TitleStatusHype
Language Models Implement Simple Word2Vec-style Vector ArithmeticCode1
Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages—0
Detecting and Mitigating Indirect Stereotypes in Word Embeddings—0
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine TranslationCode0
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic SegmentationCode1
Probing Brain Context-Sensitivity with Masked-Attention Generation—0
ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from ChatGPT-derived Context Word Embeddings—0
Word Embeddings Are Steers for Language ModelsCode1
Measuring Intersectional Biases in Historical DocumentsCode0
Controllable Speaking Styles Using a Large Language Model—0
A quantitative study of NLP approaches to question difficulty estimationCode0
Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language ModelsCode0
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic ModelingCode0
Frequency-aware Dimension Selection for Static Word Embedding by Mixed Product Distance—0
IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of ImagesCode0
Multi-Relational Hyperbolic Word Embeddings from Natural Language DefinitionsCode0
LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM—0
Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched DataCode0
Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework—0
Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings?Code0
Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine ClassifiersCode0
Semantic Frame Induction with Deep Metric Learning—0
Transcending the "Male Code": Implicit Masculine Biases in NLP Contexts—0
Word Sense Induction with Knowledge Distillation from BERT—0
Multilingual Query-by-Example Keyword Spotting with Metric Learning and Phoneme-to-Embedding Mapping—0
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