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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 1881–1890 of 4002 papers

TitleStatusHype
Indigenous Language Revitalization and the Dilemma of Gender Bias—0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition—0
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes—0
Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover's Distance Regularization—0
Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings—0
Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks v2.0—0
Affective Neural Response Generation—0
Inducing Relational Knowledge from BERT—0
Deriving continous grounded meaning representations from referentially structured multimodal contexts—0
Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach—0
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