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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 3411–3420 of 4002 papers

TitleStatusHype
Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling—0
Joint Prediction of Word Alignment with Alignment Types—0
Self-Taught Convolutional Neural Networks for Short Text ClusteringCode0
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces—0
Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling—0
Building a robust sentiment lexicon with (almost) no resource—0
Multilingual Word Embeddings using Multigraphs—0
FastText.zip: Compressing text classification modelsCode1
ConceptNet 5.5: An Open Multilingual Graph of General KnowledgeCode2
Embedding Words and Senses Together via Joint Knowledge-Enhanced Training—0
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