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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 2101–2110 of 4002 papers

TitleStatusHype
Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models?—0
Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddings—0
Revisiting Additive Compositionality: AND, OR, and NOT Operations with Word Embeddings—0
Revisiting Embedding Features for Simple Semi-supervised Learning—0
Revisiting Representation Degeneration Problem in Language Modeling—0
Revisiting Statistical Laws of Semantic Shift in Romance Cognates—0
Revisiting Supertagging and Parsing: How to Use Supertags in Transition-Based Parsing—0
Revisiting the Context Window for Cross-lingual Word Embeddings—0
Revisiting Word Embeddings in the LLM Era—0
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems—0
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