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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 2021–2030 of 4002 papers

TitleStatusHype
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base—0
QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian—0
Quality of Word Embeddings on Sentiment Analysis Tasks—0
Quantifying 60 Years of Gender Bias in Biomedical Research with Word Embeddings—0
Quantifying and Reducing Stereotypes in Word Embeddings—0
Quantifying Context Overlap for Training Word Embeddings—0
Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs—0
Quantum Algorithms for Compositional Text Processing—0
Quantum-inspired Complex Word Embedding—0
Query Expansion for Cross-Language Question Re-Ranking—0
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