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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 201–210 of 4002 papers

TitleStatusHype
Expressivity-aware Music Performance Retrieval using Mid-level Perceptual Features and Emotion Word Embeddings—0
Contrastive Learning in Distilled ModelsCode0
Multilingual acoustic word embeddings for zero-resource languages—0
GWPT: A Green Word-Embedding-based POS Tagger—0
Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed EmbeddingsCode0
Machine Learning to Promote Translational Research: Predicting Patent and Clinical Trial Inclusion in Dementia Research—0
MoSECroT: Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer—0
Estimating Text Similarity based on Semantic Concept Embeddings—0
Building Vision-Language Models on Solid Foundations with Masked Distillation—0
An Analysis of Embedding Layers and Similarity Scores using Siamese Neural Networks—0
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