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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 721–730 of 4002 papers

TitleStatusHype
Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus—0
A Review on Deep Learning Techniques Applied to Answer Selection—0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
Classifying Text-Based Conspiracy Tweets related to COVID-19 using Contextualized Word Embeddings—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
CLFD: A Novel Vectorization Technique and Its Application in Fake News Detection—0
Clickbait detection using word embeddings—0
Clinical Abbreviation Disambiguation Using Neural Word Embeddings—0
“Are you calling for the vaporizer you ordered?” Combining Search and Prediction to Identify Orders in Contact Centers—0
BLISS in Non-Isometric Embedding Spaces—0
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