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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 1811–1820 of 4002 papers

TitleStatusHype
Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence—0
CopyBERT: A Unified Approach to Question Generation with Self-Attention—0
Improved Semantic Representation for Domain-Specific Entities—0
BERTrade: Using Contextual Embeddings to Parse Old French—0
Improved Text Classification via Contrastive Adversarial Training—0
Improved Word Embeddings with Implicit Structure Information—0
Corpus specificity in LSA and Word2vec: the role of out-of-domain documents—0
Analyzing Semantic Change in Japanese Loanwords—0
Improve Lexicon-based Word Embeddings By Word Sense Disambiguation—0
BERTMap: A BERT-based Ontology Alignment System—0
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