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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 3491–3500 of 4002 papers

TitleStatusHype
Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language ProcessingCode0
One Word, Two Sides: Traces of Stance in Contextualized Word RepresentationsCode0
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity SearchCode0
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical DataCode0
1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential PrivacyCode0
Learning Crosslingual Word Embeddings without Bilingual CorporaCode0
Learning Diachronic Analogies to Analyze Concept ChangeCode0
Audio Caption in a Car Setting with a Sentence-Level LossCode0
Better Word Embeddings by Disentangling Contextual n-Gram InformationCode0
Word-Class Embeddings for Multiclass Text ClassificationCode0
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