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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 3741–3750 of 4002 papers

TitleStatusHype
Unsupervised word segmentation and lexicon discovery using acoustic word embeddings—0
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification—0
Character-based Neural Machine Translation—0
Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs—0
Easy-First Dependency Parsing with Hierarchical Tree LSTMs—0
Characterizing Diseases from Unstructured Text: A Vocabulary Driven Word2vec ApproachCode0
Representation of linguistic form and function in recurrent neural networksCode0
Toward Mention Detection Robustness with Recurrent Neural Networks—0
Ultradense Word Embeddings by Orthogonal TransformationCode0
Recovering Structured Probability Matrices—0
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