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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 2231–2240 of 4002 papers

TitleStatusHype
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data—0
Exploiting Morphological Regularities in Distributional Word Representations—0
Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers—0
Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion—0
Exploration des relations sémantiques sous-jacentes aux plongements contextuels de mots (Exploring semantic relations underlying contextual word embeddings)—0
Exploration on Grounded Word Embedding: Matching Words and Images with Image-Enhanced Skip-Gram Model—0
Exploring Adequacy Errors in Neural Machine Translation with the Help of Cross-Language Aligned Word Embeddings—0
Exploring Bilingual Word Embeddings for Hiligaynon, a Low-Resource Language—0
Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks—0
Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets—0
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