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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 1861–1870 of 4002 papers

TitleStatusHype
A Neurobiologically Motivated Analysis of Distributional Semantic Models—0
Improving Zero Shot Learning Baselines with Commonsense Knowledge—0
Initial Experiments in Data-Driven Morphological Analysis for Finnish—0
Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings—0
INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter—0
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition—0
In-Context Former: Lightning-fast Compressing Context for Large Language Model—0
Incorporating Context into Language Encoding Models for fMRI—0
Incorporating Emoji Descriptions Improves Tweet Classification—0
Affective Neural Response Generation—0
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