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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 2661–2670 of 4002 papers

TitleStatusHype
Learning bilingual word embeddings with (almost) no bilingual data—0
Learning Category Correlations for Multi-label Image Recognition with Graph Networks—0
Early Detection of Social Media Hoaxes at Scale—0
Learning Complex Word Embeddings in Classical and Quantum Spaces—0
Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases—0
Learning Conceptual Spaces with Disentangled Facets—0
Learning Connective-based Word Representations for Implicit Discourse Relation Identification—0
Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering—0
Learning Covariate-Specific Embeddings with Tensor Decompositions—0
Learning Cross-Context Entity Representations from Text—0
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