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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 2481–2490 of 4002 papers

TitleStatusHype
Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses—0
Improving Word Alignment of Rare Words with Word Embeddings—0
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models—0
Improving Word Embeddings Using Kernel PCA—0
Improving Word Representations via Global Context and Multiple Word Prototypes—0
Improving Zero Shot Learning Baselines with Commonsense Knowledge—0
INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter—0
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition—0
In-Context Former: Lightning-fast Compressing Context for Large Language Model—0
Incorporating Context into Language Encoding Models for fMRI—0
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