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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 591–600 of 4002 papers

TitleStatusHype
Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language—0
Hyperbolic Centroid Calculations for Text Classification—0
Using Large Pre-Trained Language Model to Assist FDA in Premarket Medical Device—0
Analyzing Acoustic Word Embeddings from Pre-trained Self-supervised Speech Models—0
Feature Engineering vs BERT on Twitter Data—0
Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation—0
A Robust Bias Mitigation Procedure Based on the Stereotype Content ModelCode0
Sinhala Sentence Embedding: A Two-Tiered Structure for Low-Resource Languages—0
On the Curious Case of _2 norm of Sense Embeddings—0
Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence modelsCode0
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