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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 3621–3630 of 4002 papers

TitleStatusHype
The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction—0
Actionable and Political Text Classification using Word Embeddings and LSTM—0
Representation learning for very short texts using weighted word embedding aggregationCode0
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic ResourceCode0
Learning Crosslingual Word Embeddings without Bilingual CorporaCode0
Exact gradient updates in time independent of output size for the spherical loss family—0
Using Word Embeddings for Automatic Query Expansion—0
Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation—0
Toward Word Embedding for Personalized Information Retrieval—0
Using Word Embeddings in Twitter Election Classification—0
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