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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 2141–2150 of 4002 papers

TitleStatusHype
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings—0
Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge—0
SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression—0
Selection Strategies for Commonsense Knowledge—0
Selective Co-occurrences for Word-Emotion Association—0
Self-Attention Architectures for Answer-Agnostic Neural Question Generation—0
Self-Attention for Incomplete Utterance Rewriting—0
Self-Reflective Sentiment Analysis—0
Self-Supervised Acoustic Word Embedding Learning via Correspondence Transformer Encoder—0
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case—0
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