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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 1151–1160 of 4002 papers

TitleStatusHype
Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers—0
Eliciting Explicit Knowledge From Domain Experts in Direct Intrinsic Evaluation of Word Embeddings for Specialized DomainsCode0
Evaluating Neural Word Embeddings for SanskritCode0
Handling Out-Of-Vocabulary Problem in Hangeul Word Embeddings—0
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding—0
Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey—0
Locally-Contextual Nonlinear CRFs for Sequence Labeling—0
Probabilistic Analogical Mapping with Semantic Relation Networks—0
Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications—0
An Introduction to Robust Graph Convolutional Networks—0
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