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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 711–720 of 4002 papers

TitleStatusHype
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics—0
Evaluating Metrics for Bias in Word Embeddings—0
Zero-Shot Learning in Named-Entity Recognition with External KnowledgeCode1
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning—0
Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings—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
Monitoring geometrical properties of word embeddings for detecting the emergence of new topics—0
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