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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 3751–3760 of 4002 papers

TitleStatusHype
Massively Multilingual Word EmbeddingsCode0
A Dual Embedding Space Model for Document Ranking—0
Linear Algebraic Structure of Word Senses, with Applications to PolysemyCode0
Trans-gram, Fast Cross-lingual Word-embeddings—0
From Word Embeddings to Item RecommendationCode1
The Role of Context Types and Dimensionality in Learning Word Embeddings—0
Detecting Most Frequent Sense using Word Embeddings and BabelNet—0
Word Embeddings as Metric Recovery in Semantic Spaces—0
Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error StatesCode0
Learning Semantic Similarity for Very Short Texts—0
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