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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 1241–1250 of 4002 papers

TitleStatusHype
Boosting Named Entity Recognition with Neural Character Embeddings—0
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques—0
Acoustic Word Embedding System for Code-Switching Query-by-example Spoken Term Detection—0
Dyr Bul Shchyl. Proxying Sound Symbolism With Word Embeddings—0
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora—0
Early Discovery of Disappearing Entities in Microblogs—0
Earth Mover's Distance Minimization for Unsupervised Bilingual Lexicon Induction—0
Easy-First Dependency Parsing with Hierarchical Tree LSTMs—0
Bootstrapping NLU Models with Multi-task Learning—0
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing—0
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