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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 3101–3110 of 4002 papers

TitleStatusHype
A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings—0
A Review of Cross-Domain Text-to-SQL Models—0
A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content—0
A Review on Deep Learning Techniques Applied to Answer Selection—0
Are Word Embedding-based Features Useful for Sarcasm Detection?—0
Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit?—0
“Are you calling for the vaporizer you ordered?” Combining Search and Prediction to Identify Orders in Contact Centers—0
ArGoT: A Glossary of Terms extracted from the arXiv—0
Argumentative Topology: Finding Loop(holes) in Logic—0
Argument from Old Man’s View: Assessing Social Bias in Argumentation—0
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