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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 2921–2930 of 4002 papers

TitleStatusHype
Adversarial Training for Unsupervised Bilingual Lexicon Induction—0
Adversarial Transfer Learning for Punctuation Restoration—0
A Dynamic Window Neural Network for CCG Supertagging—0
A Fast and Accurate Dependency Parser using Neural Networks—0
A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval—0
A Feature Analysis for Multimodal News Retrieval—0
Aff2Vec: Affect--Enriched Distributional Word Representations—0
Affect as a proxy for literary mood—0
Affect Enriched Word Embeddings for News Information Retrieval—0
Affective Neural Response Generation—0
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