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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 1131–1140 of 4002 papers

TitleStatusHype
Word2rate: training and evaluating multiple word embeddings as statistical transitions—0
Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task TrainingCode0
[RE] Double-Hard Debias: Tailoring Word Embeddings for Gender Bias MitigationCode0
UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction—0
On the Interpretability and Significance of Bias Metrics in Texts: a PMI-based ApproachCode0
Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoders—0
The DKU System Description for The Interspeech 2021 Auto-KWS Challenge—0
FreSaDa: A French Satire Data Set for Cross-Domain Satire DetectionCode0
Object Priors for Classifying and Localizing Unseen ActionsCode0
Machine Learning Based on Natural Language Processing to Detect Cardiac Failure in Clinical Narratives—0
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