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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 2701–2710 of 4002 papers

TitleStatusHype
WEAC: Word embeddings for anomaly classification from event logs—0
Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings—0
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection—0
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings—0
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models—0
Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels—0
WEmbSim: A Simple yet Effective Metric for Image Captioning—0
What Analogies Reveal about Word Vectors and their Compositionality—0
What are the biases in my word embedding?—0
What can you do with a rock? Affordance extraction via word embeddings—0
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