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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 2271–2280 of 4002 papers

TitleStatusHype
Span-Aggregatable, Contextualized Word Embeddings for Effective Phrase Mining—0
Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(n\^6) down to O(n\^3)—0
Spanish Biomedical and Clinical Language Embeddings—0
Spanish NER with Word Representations and Conditional Random Fields—0
Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling—0
Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling—0
SparseGAN: Sparse Generative Adversarial Network for Text Generation—0
SParse: Ko University Graph-Based Parsing System for the CoNLL 2018 Shared Task—0
Sparsifying Word Representations for Deep Unordered Sentence Modeling—0
Spatio-temporal Sign Language Representation and Translation—0
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