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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 891–900 of 4002 papers

TitleStatusHype
Contrastive Word Embedding Learning for Neural Machine Translation—0
ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks—0
Attending Sentences to detect Satirical Fake News—0
Convolutional Neural Network for Universal Sentence Embeddings—0
Abstractive Document Summarization with Word Embedding Reconstruction—0
Convolutional Neural Networks for Sentiment Classification on Business Reviews—0
Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach—0
Attention-based model for predicting question relatedness on Stack Overflow—0
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization—0
Cross-Lingual Word Embeddings for Low-Resource Language Modeling—0
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