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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 1511–1520 of 4002 papers

TitleStatusHype
A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction—0
Transition-based Semantic Dependency Parsing with Pointer Networks—0
Getting the \#\#life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?—0
Supervised Understanding of Word Embeddings—0
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings—0
Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations—0
Learning aligned embeddings for semi-supervised word translation using Maximum Mean Discrepancy—0
MDR Cluster-Debias: A Nonlinear WordEmbedding Debiasing Pipeline—0
On the Learnability of Concepts: With Applications to Comparing Word Embedding Algorithms—0
Evaluating a Multi-sense Definition Generation Model for Multiple Languages—0
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