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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 3231–3240 of 4002 papers

TitleStatusHype
BERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation—0
BERT-based Ranking for Biomedical Entity Normalization—0
BERTMap: A BERT-based Ontology Alignment System—0
BERTrade: Using Contextual Embeddings to Parse Old French—0
BERT's Conceptual Cartography: Mapping the Landscapes of Meaning—0
BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets—0
Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings—0
Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification—0
Better OOV Translation with Bilingual Terminology Mining—0
Better Word Representations with Recursive Neural Networks for Morphology—0
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