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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 3071–3080 of 4002 papers

TitleStatusHype
``A Passage to India'': Pre-trained Word Embeddings for Indian Languages—0
"A Passage to India": Pre-trained Word Embeddings for Indian Languages—0
A Platform Agnostic Dual-Strand Hate Speech Detector—0
A polar coordinate system represents syntax in large language models—0
Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction—0
Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task—0
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data—0
Applying Multi-Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language—0
Applying Occam’s Razor to Transformer-Based Dependency Parsing: What Works, What Doesn’t, and What is Really Necessary—0
Applying Word Embeddings to Measure Valence in Information Operations Targeting Journalists in Brazil—0
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