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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 1161–1170 of 4002 papers

TitleStatusHype
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models—0
Multimodal Review Generation with Privacy and Fairness AwarenessCode0
Affective and Contextual Embedding for Sarcasm DetectionCode1
Neural Networks approaches focused on French Spoken Language Understanding: application to the MEDIA Evaluation TaskCode0
A Simple and Effective Usage of Word Clusters for CBOW ModelCode0
A Review of Cross-Domain Text-to-SQL Models—0
BERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation—0
Explaining Word Embeddings via Disentangled Representation—0
Learning Negation Scope from Syntactic Structure—0
Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings—0
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