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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 3181–3190 of 4002 papers

TitleStatusHype
AutoExtend: Combining Word Embeddings with Semantic Resources—0
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes—0
Automated Detection of Adverse Drug Reactions in the Biomedical Literature Using Convolutional Neural Networks and Biomedical Word Embeddings—0
Automated Discovery of Mathematical Definitions in Text—0
Automated Discovery of Mathematical Definitions in Text with Deep Neural Networks—0
Automated essay scoring with string kernels and word embeddings—0
Automated Image Captioning for Rapid Prototyping and Resource Constrained Environments—0
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach—0
Automated Preamble Detection in Dictated Medical Reports—0
Automated Scoring of Clinical Expressive Language Evaluation Tasks—0
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