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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 1391–1400 of 4002 papers

TitleStatusHype
A Feature Analysis for Multimodal News Retrieval—0
An Enhanced Text Classification to Explore Health based Indian Government Policy Tweets—0
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning—0
Topic Modeling on User Stories using Word Mover's DistanceCode0
GLOW : Global Weighted Self-Attention Network for Web SearchCode1
Cultural Cartography with Word Embeddings—0
Automatic Detection of Sexist Statements Commonly Used at the WorkplaceCode0
Contextualized Spoken Word Representations from Convolutional Autoencoders—0
Reflection-based Word Attribute TransferCode0
Tweets Sentiment Analysis via Word Embeddings and Machine Learning Techniques—0
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