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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 881–890 of 4002 papers

TitleStatusHype
Discrete Wavelet Transform for Efficient Word Embeddings and Sentence Encoding—0
Evaluating Metrics for Bias in Word Embeddings—0
Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings—0
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning—0
PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed LanguagesCode0
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach—0
Training Cross-Lingual embeddings for Setswana and SepediCode0
Topic-aware latent models for representation learning on networks—0
SocialVec: Social Entity EmbeddingsCode0
Monitoring geometrical properties of word embeddings for detecting the emergence of new topics—0
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