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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 1361–1370 of 4002 papers

TitleStatusHype
Contrastive Visual Semantic Pretraining Magnifies the Semantics of Natural Language Representations—0
A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation—0
Continuous Word Embedding Fusion via Spectral Decomposition—0
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings—0
A Distribution-based Model to Learn Bilingual Word Embeddings—0
Context Vectors are Reflections of Word Vectors in Half the Dimensions—0
A Topical Approach to Capturing Customer Insight In Social Media—0
Analysis of Word Embeddings Using Fuzzy Clustering—0
CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning—0
Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge—0
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