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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 1991–2000 of 4002 papers

TitleStatusHype
Evaluation of Morphological Embeddings for the Russian Language—0
Joint Learning of Hierarchical Word Embeddings from a Corpus and a Taxonomy—0
Evaluation of Greek Word Embeddings—0
Joint Learning of Word and Label Embeddings for Sequence Labelling in Spoken Language Understanding—0
Jointly Learning to Embed and Predict with Multiple Languages—0
JOINTLY LEARNING TOPIC SPECIFIC WORD AND DOCUMENT EMBEDDING—0
Classification of Micro-Texts Using Sub-Word Embeddings—0
Jointly modelling the evolution of social structure and language in online communities—0
A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content—0
A Locally Linear Procedure for Word Translation—0
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