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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 1811–1820 of 4002 papers

TitleStatusHype
Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence—0
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization—0
Clustering is Efficient for Approximate Maximum Inner Product Search—0
Exploiting Class Labels to Boost Performance on Embedding-based Text Classification—0
Clustering Comparable Corpora of Russian and Ukrainian Academic Texts: Word Embeddings and Semantic Fingerprints—0
Improved Word Embeddings with Implicit Structure Information—0
Explaining Word Embeddings via Disentangled Representation—0
Explaining the Trump Gap in Social Distancing Using COVID Discourse—0
Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis—0
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning—0
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