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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 1281–1290 of 4002 papers

TitleStatusHype
Automated Detection of Adverse Drug Reactions in the Biomedical Literature Using Convolutional Neural Networks and Biomedical Word Embeddings—0
An Analysis of Embedding Layers and Similarity Scores using Siamese Neural Networks—0
Adversarial Contrastive Estimation—0
A Comparative Study of Word Embeddings for Reading Comprehension—0
Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks v2.0—0
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes—0
Cross-lingual Linking of Automatically Constructed Frames and FrameNet—0
AutoExtend: Combining Word Embeddings with Semantic Resources—0
An Analysis of Deep Contextual Word Embeddings and Neural Architectures for Toponym Mention Detection in Scientific Publications—0
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation—0
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