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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 3591–3600 of 4002 papers

TitleStatusHype
Dependency Based Embeddings for Sentence Classification Tasks—0
Dependency-Based Semantic Role Labeling using Convolutional Neural Networks—0
Dependency-Based Word Embeddings—0
Dependency Link Embeddings: Continuous Representations of Syntactic Substructures—0
Dependency Parsing for Urdu: Resources, Conversions and Learning—0
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media—0
Derivational Morphological Relations in Word Embeddings—0
Derivational Morphological Relations in Word Embeddings—0
Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings—0
Deriving continous grounded meaning representations from referentially structured multimodal contexts—0
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