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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 1481–1490 of 4002 papers

TitleStatusHype
Cross-Lingual Word Embeddings for Turkic LanguagesCode0
Learning Probabilistic Sentence Representations from Paraphrases—0
Towards classification parity across cohorts—0
RPD: A Distance Function Between Word Embeddings—0
Cross-lingual Transfer of Sentiment Classifiers—0
Evaluating Sparse Interpretable Word Embeddings for Biomedical DomainCode0
Pretraining Federated Text Models for Next Word PredictionCode1
Toward Better Storylines with Sentence-Level Language Models—0
Article citation study: Context enhanced citation sentiment detection—0
Comparative Analysis of Word Embeddings for Capturing Word SimilaritiesCode0
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