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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 2681–2690 of 4002 papers

TitleStatusHype
Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks—0
Learning Emotion-enriched Word Representations—0
Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories—0
Learning finite state word representations for unsupervised Twitter adaptation of POS taggers—0
Learning Graph-Based Priors for Generalized Zero-Shot Learning—0
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis—0
Learning Joint Acoustic-Phonetic Word Embeddings—0
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings—0
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings—0
Learning Mixed-Curvature Representations in Product Spaces—0
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