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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 2781–2790 of 4002 papers

TitleStatusHype
Word Embeddings from Large-Scale Greek Web Content—0
Word Embeddings Inherently Recover the Conceptual Organization of the Human Mind—0
Word Embeddings Revisited: Do LLMs Offer Something New?—0
Word Embeddings: Stability and Semantic Change—0
Word Embeddings through Hellinger PCA—0
Word Embeddings to Enhance Twitter Gang Member Profile Identification—0
Word Embeddings Track Social Group Changes Across 70 Years in China—0
Word Embeddings vs Word Types for Sequence Labeling: the Curious Case of CV Parsing—0
Word Embeddings with Limited Memory—0
Word Embedding Techniques for Classification of Star Ratings—0
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