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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 261–270 of 4002 papers

TitleStatusHype
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data—0
Applying Occam’s Razor to Transformer-Based Dependency Parsing: What Works, What Doesn’t, and What is Really Necessary—0
An Efficient Cross-lingual Model for Sentence Classification Using Convolutional Neural Network—0
Anchor-based Bilingual Word Embeddings for Low-Resource Languages—0
Adversarial Representation Learning for Text-to-Image Matching—0
An Automatic Learning of an Algerian Dialect Lexicon by using Multilingual Word Embeddings—0
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings—0
Examining Structure of Word Embeddings with PCA—0
An Automated Method to Enrich Consumer Health Vocabularies Using GloVe Word Embeddings and An Auxiliary Lexical Resource—0
An Attentive Fine-Grained Entity Typing Model with Latent Type Representation—0
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