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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 3771–3780 of 4002 papers

TitleStatusHype
Learning Articulated Motion Models from Visual and Lingual Signals—0
Learning the Dimensionality of Word Embeddings—0
An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding—0
Controlled Experiments for Word EmbeddingsCode0
Mapping Unseen Words to Task-Trained Embedding Spaces—0
Deep convolutional acoustic word embeddings using word-pair side informationCode0
Bidirectional Long Short-Term Memory Networks for Relation Classification—0
Reducing Lexical Features in Parsing by Word Embeddings—0
Bilingual Distributed Word Representations from Document-Aligned Comparable Data—0
Word, graph and manifold embedding from Markov processes—0
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