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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 291–300 of 4002 papers

TitleStatusHype
Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models—0
Analyzing Continuous Semantic Shifts with Diachronic Word Similarity MatricesCode0
A Multi-tiered Solution for Personalized Baggage Item Recommendations using FastText and Association Rule Mining—0
Integrating Pause Information with Word Embeddings in Language Models for Alzheimer's Disease Detection from Spontaneous Speech—0
Signatures of prediction during natural listening in MEG data?—0
VITRO: Vocabulary Inversion for Time-series Representation Optimization—0
EF-Net: A Deep Learning Approach Combining Word Embeddings and Feature Fusion for Patient Disposition AnalysisCode0
Learning Complex Word Embeddings in Classical and Quantum Spaces—0
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection—0
Quantifying Lexical Semantic Shift via Unbalanced Optimal TransportCode0
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