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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 3451–3460 of 4002 papers

TitleStatusHype
Distributional Hypernym Generation by Jointly Learning Clusters and Projections—0
Enriching Phrase Tables for Statistical Machine Translation Using Mixed Embeddings—0
Sub-Word Similarity based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modelling—0
Latent Topic Embedding—0
Improved Word Embeddings with Implicit Structure Information—0
Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network—0
Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs—0
Structured Generative Models of Continuous Features for Word Sense Induction—0
Different Contexts Lead to Different Word Embeddings—0
Bilingual Autoencoders with Global Descriptors for Modeling Parallel Sentences—0
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