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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 3581–3590 of 4002 papers

TitleStatusHype
Random Positive-Only Projections: PPMI-Enabled Incremental Semantic Space Construction—0
context2vec: Learning Generic Context Embedding with Bidirectional LSTM—0
When Hyperparameters Help: Beneficial Parameter Combinations in Distributional Semantic Models—0
Unsupervised Text Segmentation Using Semantic Relatedness GraphsCode0
Incorporating Relational Knowledge into Word Representations using Subspace Regularization—0
Cross-Lingual Word Representations via Spectral Graph EmbeddingsCode0
Chinese Zero Pronoun Resolution with Deep Neural Networks—0
Is ``Universal Syntax'' Universally Useful for Learning Distributed Word Representations?—0
Word Embedding Calculus in Meaningful Ultradense Subspaces—0
A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings—0
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