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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 3811–3820 of 4002 papers

TitleStatusHype
Estimating word co-occurrence probabilities from pretrained static embeddings using a log-bilinear model—0
Estimator Vectors: OOV Word Embeddings based on Subword and Context Clue Estimates—0
\'Etiquetage en parties du discours de langues peu dot\'ees par sp\'ecialisation des plongements lexicaux (POS tagging for low-resource languages by adapting word embeddings )—0
Etude de la reproductibilit\'e des word embeddings : rep\'erage des zones stables et instables dans le lexique (Reproducibility of word embeddings : identifying stable and unstable zones in the semantic space)—0
\'Etude sur le r\'esum\'e comparatif gr\^ace aux plongements de mots (Comparative summarization study using word embeddings)—0
EusDisParser: improving an under-resourced discourse parser with cross-lingual data—0
Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages—0
Evaluating a Multi-sense Definition Generation Model for Multiple Languages—0
Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus—0
Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesaurus Using Siamese Networks—0
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