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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 951–960 of 4002 papers

TitleStatusHype
Variational Gaussian Topic Model with Invertible Neural Projections—0
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning—0
Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media—0
TF-IDF vs Word Embeddings for Morbidity Identification in Clinical Notes: An Initial StudyCode0
An Automated Method to Enrich Consumer Health Vocabularies Using GloVe Word Embeddings and An Auxiliary Lexical Resource—0
KECRS: Towards Knowledge-Enriched Conversational Recommendation System—0
Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddings—0
WOVe: Incorporating Word Order in GloVe Word Embeddings—0
Coming to its senses: Lessons learned from Approximating Retrofitted BERT representations for Word Sense information—0
Empirical Analysis of Image Caption Generation using Deep Learning—0
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