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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 2571–2580 of 4002 papers

TitleStatusHype
Unsupervised Induction of Linguistic Categories with Records of Reading, Speaking, and Writing—0
Unsupervised Inference of Object Affordance from Text Corpora—0
Unsupervised Joint Training of Bilingual Word Embeddings—0
Unsupervised Learning of Distributional Relation Vectors—0
Unsupervised Learning of Entailment-Vector Word Embeddings—0
Unsupervised Learning of Style-sensitive Word Vectors—0
Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding—0
Unsupervised Morphological Expansion of Small Datasets for Improving Word Embeddings—0
Unsupervised Morphology Induction Using Word Embeddings—0
Unsupervised Most Frequent Sense Detection using Word Embeddings—0
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