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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 3441–3450 of 4002 papers

TitleStatusHype
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop—0
Connecting Supervised and Unsupervised Sentence Embeddings—0
Considerations for the Interpretation of Bias Measures of Word Embeddings—0
Consistency and Variation in Kernel Neural Ranking Model—0
Consistent Structural Relation Learning for Zero-Shot Segmentation—0
Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings—0
Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense—0
Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals—0
Content-Aware Speaker Embeddings for Speaker Diarisation—0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
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