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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 2001–2010 of 4002 papers

TitleStatusHype
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge—0
Proactive Security: Embedded AI Solution for Violent and Abusive Speech Recognition—0
Probabilistic Analogical Mapping with Semantic Relation Networks—0
Probabilistic Bias Mitigation in Word Embeddings—0
Probabilistic Embeddings with Laplacian Graph Priors—0
Probabilistic Lexical Manifold Construction in Large Language Models via Hierarchical Vector Field Interpolation—0
Probabilistic Relation Induction in Vector Space Embeddings—0
Probing Brain Context-Sensitivity with Masked-Attention Generation—0
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?—0
Probing the statistical properties of enriched co-occurrence networks—0
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