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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 2191–2200 of 4002 papers

TitleStatusHype
Distribution is not enough: going Firther—0
Nested Variational Autoencoder for Topic Modeling on Microtexts with Word VectorsCode0
Wasserstein Barycenter Model Ensembling—0
Zero-training Sentence Embedding via Orthogonal BasisCode0
RelWalk -- A Latent Variable Model Approach to Knowledge Graph Embedding—0
The Effectiveness of Pre-Trained Code Embeddings—0
Unsupervised Hyper-alignment for Multilingual Word Embeddings—0
Encoding Category Trees Into Word-Embeddings Using Geometric ApproachCode0
Learning Mixed-Curvature Representations in Product Spaces—0
Poincare Glove: Hyperbolic Word EmbeddingsCode0
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