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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 2791–2800 of 4002 papers

TitleStatusHype
Word Embedding Transformation for Robust Unsupervised Bilingual Lexicon Induction—0
Word Emdeddings through Hellinger PCA—0
Word Equations: Inherently Interpretable Sparse Word Embeddings through Sparse Coding—0
WordForce: Visualizing Controversial Words in Debates—0
Word, graph and manifold embedding from Markov processes—0
Word-level Speech Recognition with a Letter to Word Encoder—0
Wordnet-based Evaluation of Large Distributional Models for Polish—0
Wordnet extension via word embeddings: Experiments on the Norwegian Wordnet—0
Word Re-Embedding via Manifold Dimensionality Retention—0
Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings—0
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