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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 61–70 of 4002 papers

TitleStatusHype
Astro-HEP-BERT: A bidirectional language model for studying the meanings of concepts in astrophysics and high energy physics—0
Meaning at the Planck scale? Contextualized word embeddings for doing history, philosophy, and sociology of science—0
Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot LearningCode1
Mitigating Gender Bias in Contextual Word Embeddings—0
HJ-Ky-0.1: an Evaluation Dataset for Kyrgyz Word EmbeddingsCode1
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
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