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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 311–320 of 4002 papers

TitleStatusHype
Mitigating Gender Bias in Contextual Word Embeddings—0
A Primer on Word Embeddings: AI Techniques for Text Analysis in Social Work—0
Discovering emergent connections in quantum physics research via dynamic word embeddingsCode0
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models—0
Social Support Detection from Social Media Texts—0
NLP and Education: using semantic similarity to evaluate filled gaps in a large-scale Cloze test in the classroom—0
Generic Embedding-Based Lexicons for Transparent and Reproducible Text Scoring—0
Zipfian WhiteningCode0
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers—0
Tracing the Development of the Virtual Particle Concept Using Semantic Change DetectionCode0
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