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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 441–450 of 4002 papers

TitleStatusHype
Zur Darstellung eines mehrstufigen Prototypbegriffs in der multilingualen automatischen Sprachgenerierung: vom Korpus über word embeddings bis hin zum automatischen Wörterbuch—0
Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling—0
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models—0
Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations—0
Disentangling continuous and discrete linguistic signals in transformer-based sentence embeddings—0
Def2Vec: Extensible Word Embeddings from Dictionary DefinitionsCode0
Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels—0
Natural Language Processing for Diagnosis and Risk Assessment of Cardiovascular Disease—0
Where exactly does contextualization in a PLM happen?—0
Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals—0
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