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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 2011–2020 of 4002 papers

TitleStatusHype
Distilled Wasserstein Learning for Word Embedding and Topic Modeling—0
Distilling Word Embeddings: An Encoding Approach—0
Distinguishing Japanese Non-standard Usages from Standard Ones—0
Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible—0
Distributed Representations for Unsupervised Semantic Role Labeling—0
Distributed Vector Representations for Unsupervised Automatic Short Answer Grading—0
Distributed Training of Embeddings using Graph Analytics—0
Distributional Analysis of Polysemous Function Words—0
Distributional Hypernym Generation by Jointly Learning Clusters and Projections—0
Distributional Lesk: Effective Knowledge-Based Word Sense Disambiguation—0
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