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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 3591–3600 of 4002 papers

TitleStatusHype
Investigating Language Universal and Specific Properties in Word Embeddings—0
Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time—0
Roleo: Visualising Thematic Fit Spaces on the Web—0
Language Transfer Learning for Supervised Lexical Substitution—0
A Domain Adaptation Regularization for Denoising Autoencoders—0
Singleton Detection using Word Embeddings and Neural Networks—0
Word Embeddings with Limited Memory—0
Embeddings for Word Sense Disambiguation: An Evaluation StudyCode0
Bidirectional Recurrent Convolutional Neural Network for Relation Classification—0
An Efficient Cross-lingual Model for Sentence Classification Using Convolutional Neural Network—0
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