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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 1081–1090 of 4002 papers

TitleStatusHype
Misspelling Correction with Pre-trained Contextual Language Model—0
Political Depolarization of News Articles Using Attribute-aware Word Embeddings—0
Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping—0
Lex-BERT: Enhancing BERT based NER with lexicons—0
Text Document Clustering: Wordnet vs. TF-IDF vs. Word Embeddings—0
Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions—0
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide ImagesCode1
Kernel Methods in Hyperbolic Spaces—0
Visual-Textual Attentive Semantic Consistency for Medical Report Generation—0
Key Phrase Extraction & Applause Prediction—0
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