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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 351–360 of 4002 papers

TitleStatusHype
ML-EAT: A Multilevel Embedding Association Test for Interpretable and Transparent Social ScienceCode0
Representation Bias of Adolescents in AI: A Bilingual, Bicultural StudyCode0
Misinforming LLMs: vulnerabilities, challenges and opportunities—0
Ontological Relations from Word Embeddings—0
You shall know a piece by the company it keeps. Chess plays as a data for word2vec models—0
Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction—0
The BIAS Detection Framework: Bias Detection in Word Embeddings and Language Models for European LanguagesCode0
On Initializing Transformers with Pre-trained Embeddings—0
R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models—0
Cross-Lingual Word Alignment for ASEAN Languages with Contrastive Learning—0
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